We're hiring a senior front-end contractor to take ownership of the front end of CMMC.builders, a production compliance-assessment platform, and then support other applications in our portfolio on an ongoing basis. The app was built quickly using Replit and AI-assisted ("vibe coding") workflows — it works, is in production, but it was not written with long-term maintainability as the top priority. We need someone who can read a large AI-generated codebase, form an independent judgment about what's solid versus fragile, and systematically bring it up to a level you'd defend in a code review at a company that values engineering. This isn’t a "rewrite everything" job. It's an audit-and-refactor process: understand what exists, identify risks, fix them without breaking production, and leave the codebase in a state a regular team could safely build on. This is a long-term role supporting a well-funded, high-stakes MVP, with real potential to turn into a full-time position for the right person. WHAT YOU'LL ACTUALLY BE DOING Phase 1 — Audit (first 1–2 weeks) Review the front-end architecture (component structure, state management, data-fetching patterns, routing, type safety) and prepare a written report on your findings, prioritized by risk and effort. Identify AI-coding issues specifically: duplicated logic, inconsistent patterns across similar features, overly broad "any" typing, prop-drilling where shared state should be, dead code, and components that mix data-fetching, business logic, and presentation. Flag any security or correctness concerns on the front end (unsanitized rendering, client-side trust of server-controlled data, broken access-control assumptions). Phase 2 — Refactor (ongoing) Carry out the refactor plan in reviewable, incremental PRs — no large-scale rewrites. Establish or tighten conventions: component boundaries, shared hooks, data-fetching layer, form/validation patterns, error and loading states. Improve type safety and eliminate unsound patterns introduced by AI-assisted coding. Add or enhance test coverage on the areas you modify. Document decisions throughout to keep the codebase understandable for the next person (human or AI). Beyond CMMC.builders Once the initial audit and top-priority refactoring are stable, you will work on front-end tasks across other applications in our portfolio — developing new features, conducting additional audits, and providing senior front-end support. OUR STACK React 19 + TypeScript, built with Vite TanStack Query for data-fetching and caching, wouter for routing Zod for schema validation Express (Node) backend, PostgreSQL via pg (no ORM — handwritten parameterized SQL) Vitest + Playwright for testing Hosted on Replit, deployed via Replit Autoscale YOU SHOULD APPLY IF You have 5+ years of production React/TypeScript experience and can cite real shipped projects, not just tutorials. You've performed codebase audits or major refactors before — not just greenfield projects. You can share a time when you inherited a messy codebase and what you actually changed. You are comfortable reading unfamiliar code quickly, forming your own judgment, and respectfully pushing back if something seems wrong — including AI-generated code that looks superficially okay. You write clearly. The audit report and PR descriptions are as important as the code. You can work independently with minimal oversight and communicate proactively, not just when prompted. NICE TO HAVE Direct experience refactoring or "productionizing" AI-generated or AI-assisted code. Familiarity with compliance, security, or regulated-industry software (this product deals with CMMC/cybersecurity compliance data). Experience using Replit as a development environment. HOW TO APPLY Your proposal must include: A one- or two-sentence example of a real audit or refactor you led on an existing codebase — what was wrong and what you did. A link to a GitHub repo or code sample showing your TypeScript/React coding style (not just a live demo link). The words "compliance refactor" are somewhere in your opening line, so we know you’ve read this posting. Proposals that are templated, copy-pasted, or don't answer the above won’t be considered, regardless of rate.

Senior Full Stack Developer — AI Platform Hardening (Trial-to-Hire) HillDave Labs builds an AI-driven platform for ad content and media buying, backed by 30 years of Fortune 500 production experience (Coca-Cola, PepsiCo, Microsoft, Disney). We're a lean, founder-led team of two heading toward a self-serve platform for brands and agencies. The platform works today but is fragile in places. We need a senior engineer to take ownership, fix what's brittle, and bring real engineering rigor before we open it to self-serve users at scale. Starts as contract work with a path to full-time once we see strong fit. What you'll do: Audit the codebase and fix the highest-risk fragility Harden core modules for reliable multi-user use Own architecture as we shift toward a self-serve product Work directly with founders, no layers What we need: Senior full stack experience, front-end to database Proven record stabilizing existing codebases Strong communicator US time zone, fluent English, real-time availability Comfortable with startup ambiguity and pace Engagement: 25-30 hrs/week, $85-95/hr, 8-12 week trial. Strong fit leads to full-time with salary, equity, and benefits. To apply: describe a time you inherited a messy codebase and made it trustworthy. What did you prioritize? Include your time zone and rate.

We are looking for an experienced DevOps engineer to deploy our existing containerized application to AWS EKS. Project Scope: - Set up a production-grade AWS EKS cluster (using eksctl or Terraform) - Deploy our microservices application using Kubernetes manifests or Helm charts - Configure proper networking (Ingress, ALB), service discovery, and secrets management - Implement Horizontal Pod Autoscaler (HPA) and resource requests/limits - Set up monitoring (CloudWatch or Prometheus) and logging - Create a basic CI/CD pipeline using GitHub Actions that builds Docker images and deploys to EKS (staging + production environments with manual approval) - Document the full deployment process and provide handover Required Skills & Experience: - Strong hands-on experience with AWS EKS and Kubernetes - Proficiency with Docker and multi-stage Dockerfiles - Experience with Helm charts (preferred) - GitHub Actions for CI/CD - Terraform or eksctl for infrastructure - AWS services: ECR, IAM, VPC, ALB - Strong Linux and YAML skills Nice to Have: - Previous experience deploying Node.js / Python / Java applications - Knowledge of ArgoCD or Flux for GitOps

Hello, I'm looking for an experienced automation engineer or systems integrator to build a cloud-based automated messaging system for our restaurant operations. We operate 10 restaurant locations with approximately 40 Motorola TLK 25 radios using WAVE PTX. The goal is to use Radio.co to schedule already created operational voice mp3 reminders (opening procedures, food safety checks, labor reviews, closing tasks, etc.) and automatically broadcast those messages to designated WAVE PTX talk groups. The proposed solution would utilize: * Radio.co for scheduling and audio content * Azure Windows VM as a dedicated cloud-hosted dispatch workstation * WAVE PTX Dispatch for radio communications * Virtual audio routing and PTT automation to transmit scheduled messages to Motorola TLK 25 devices We're looking for someone who can design, deploy, test, and document the complete solution, including Azure configuration, audio routing, automation, monitoring, and scalability for future growth. If you have experience with cloud infrastructure, automation, streaming audio, dispatch systems, or Motorola WAVE PTX, please let me know how you would approach this project. We have a clear vision and proposed architecture for this project and are looking for a skilled developer to help execute, validate, and deploy it. The objective is to create a cloud-based automated messaging system that uses Radio.co for scheduled voice reminders and broadcasts those messages to Motorola TLK 25 radios through WAVE PTX Dispatch. We anticipate utilizing an Azure Windows VM, virtual audio routing, and automated Push-to-Talk activation to create a reliable, scalable solution for restaurant operations. At this stage, we need a technical expert who can review our proposed workflow, identify any improvements or challenges, and then build, test, document, and deploy the solution. The ideal candidate has experience with cloud infrastructure, automation, audio streaming, browser automation, dispatch systems, or Motorola WAVE PTX. We value practical execution and problem-solving and are looking for someone who can take an existing concept and turn it into a production-ready system.

IF YOU CALL MY PHONE OR EMAIL ME YOU WILL BE DISQUALIFIED. FIXED PRICE DELIVERABLES ONLY. SORRY. NO HOURLY. We run SWAGO (swago.com) — a custom-apparel/merch e-commerce storefront — and we're turning its platform into MerchOS (merchos.io), a multi-tenant SaaS where any brand can spin up their own branded merch storefront on a subdomain (brand.merchos.io). SWAGO becomes the flagship tenant; the same codebase serves unlimited tenants. This is not greenfield. A substantial foundation already exists — we need a senior engineer to take it across the finish line to a production, self-serve multi-tenant launch. What's already built Host-based tenant routing (flagship / SaaS / tenant subdomains), tenant context, per-host branding. Tenant data model + row-level-security isolation (Postgres RLS): tenants, members, invites, platform admins, orders/carts/leads scoped by tenant_id. Tenant signup + Stripe Connect onboarding; company-store checkout wired to destination charges + platform application fees. Per-tenant catalog overlay and per-tenant margin/MOQ pricing scaffolding; tenant theming data model + asset storage bucket. What we need you to do (the heavy lift) Finish self-serve tenant commerce — tenant storefront (browse → cart → checkout) fully wired to Stripe Connect + tenant-scoped orders; activation/go-live. Payments hardening — Connect refunds & dispute/chargeback handling, account.updated/payout.failed Connect webhooks, per-tenant tax, and SaaS subscription billing (tiered plans/monthly fees, dunning, upgrades/cancellations). White-label — tenant branding editor UI (logo/favicon/hero/colors/portfolio), custom domains (shop.brand.com w/ cert provisioning), and per-tenant transactional email sending domains (Resend). Isolation & lifecycle completeness — audit remaining tables/storage for tenant scoping, tenant suspension/offboarding/data-export, quotas/rate limits, and content moderation. Ops — per-tenant observability, platform-admin impersonation/support tooling, audit logging. Tech stack (you must be strong in these) React (React Router v7, framework/SPA mode, TypeScript), Supabase (Postgres, RLS, Edge Functions/Deno, Auth), Stripe Connect (destination charges, application fees, webhooks), Vercel (deploys, domains, edge/rewrites), Resend. Ideal candidate Has shipped a real multi-tenant SaaS (subdomain + custom-domain routing, tenant isolation). Deep Stripe Connect experience (marketplaces/platforms), including refunds/disputes/payouts. Fluent in Postgres RLS and secure multi-tenant data modeling. Comfortable owning ambiguous, cross-cutting infra work end-to-end. Screening questions (please answer to apply) Link a multi-tenant app you built. How did you handle tenant isolation and custom domains? In Stripe Connect, who bears the fee/liability on a refund and a chargeback, and how do you implement it? How would you enforce per-tenant data isolation in Postgres so one tenant can never read another's orders?

Project Budget: $500 (Strictly Milestone-Based) CRITICAL REQUIREMENT BEFORE APPLYING: Payment for this project is strictly tied to real-world performance metrics. Milestone 1 requires a live stopwatch test on a mobile device showing sub-1-second cache loading on repeat lookups. If you do not have deep experience with high-speed local database architecture and caching models, do not apply. We measure deliverables with a stopwatch, not excuses. Project Overview & The Long-Term Vision: I am building Reseller Bro, a powerful mobile utility application designed for on-the-go resellers to instantly analyze product values, get a FLIP/SKIP verdict, and SAVE to a digital cart in seconds. This app is just the initial foundation—the engineer who successfully delivers this backend infrastructure will have the opportunity to partner with us long-term to build out our entire ecosystem, including advanced B2B inventory management tools and our wearable AR glasses workflow (Bro Lens). The front-end user interface and layout are already mostly complete. We are anchoring our backend data pipeline to a high-speed eBay API model that automatically calculates smart market estimates for other secondary platforms. We need an expert developer to clean up our database cache, implement a tier-condition formula, handle minor UI adjustments, and add high-energy audio/vibration triggers. The Core Tasks & Milestone Payment Structure: Milestone 1: Sub-1-Second Database Caching ($100 Escrow) The Issue: The previous build incorrectly forced live AI image recognition to run on every single scan, causing a 7–8 second delay even on repeat lookups. The Fix: You must implement a proper local database caching layer (e.g., SQLite). The first initial scan of an item can take up to 7 seconds to run the AI workflow and fetch the initial marketplace data. However, on any repeat scan, the app must skip the AI image recognition entirely, read a cached unique text identifier/key, and instantly pull the results from the internal database in under 1 second. Milestone 2: Data Engine & Percentage-Based Estimation Matrix ($200 Escrow) The Fix: Connect the backend cleanly with the official eBay Browse API using our developer keys. Target Marketplaces: The app displays valuation metrics for four core platforms: eBay, Depop, Grailed, and Poshmark. Condition Matrix & Platform Estimation Formula: Because eBay utilizes a wide variety of specific conditions across different categories, you will build an automated mapping and calculation formula. The app will pull raw condition data initially from the eBay API, group it cleanly into our 3-tier user system, and then use those baselines to instantly calculate the estimated market values for the other three platforms (Depop, Grailed, and Poshmark). New Tier: Dynamically maps all brand-new and pristine variations data directly, including: "New", "Excellent", "Excellent - Refurbished", "Open box", "New with box", "New with defects", and "New without box". Good Tier: Dynamically maps all standard pre-owned and quality-certified variations data directly, including: "Very Good", "Good", "Used", "Very Good - Refurbished", "Good - Refurbished", "Pre-Owned", and "Certified Pre-Owned". Poor Tier: Maps heavy-wear options directly, such as "For parts or not working" or "Fair". If a specific item category lacks a true "poor" marketplace data option, the engine must automatically fall back to calculate a custom percentage markdown (e.g., 40% less) relative to that item's "Good" tier baseline. The results from these three tiers will automatically calculate estimated market values for Depop, Grailed, and Poshmark using an internal background multiplier. If a user wants to check the exact live screen on those blocked platforms, tapping a platform tile will trigger a direct, one-tap deep link search into that specific app or web page. Smart Category Specifications (Vehicle & Electronics Handling): If the AI detects an image of a Vehicle (cars, trucks) or high-value Electronics, the app must dynamically generate a quick-spec form for the user to confirm/fill in (Vehicles: Make, Model, Year, Mileage; Electronics: Brand, Model, Capacity). This structured data must be fed directly into the pricing API for precise accuracy. Milestone 3: UI Redesigns, Audio/Haptics, Live Deployment & Final Handoff ($200 Escrow) UI Tweaks: Implement minor visual layout edits and updates to a few existing UI screens to align with this new calculation model. This includes ensuring tiles are accurately labeled as "Estimated Value," verifying the condition buttons display perfectly, adjusting the selling platform logos/designs, and making a few structural changes to the "Saved Items" page (I will go over the exact design changes with you directly). Haptics & Custom Audio Cues: Implement device vibration triggers (via Web Vibrations API) to pulse the device exactly when a verdict hits the screen. Integrate custom short audio sound bites that trigger instantly on the verdict display: a high-energy "YEAH!" (Lil Jon style) sound bite for a FLIP verdict, and a "HELL NO!" sound bite for a SKIP verdict. Live Deployment: Once the final features are fully approved, you will be responsible for successfully deploying the production build live onto our hosting account so the application is fully operational. Clean up the codebase and hand over the complete, finalized source repository. Requirements: Deep expertise in backend optimization, API data pipelines, and high-speed local database caching. Proficiency in mobile development frameworks, front-end audio integration, and Node.js. Strong communication skills. You will work with an existing repository and must provide clean, documented code. To Apply: Please reply explaining exactly how you will structure the local database cache so that a repeat scan completely bypasses the image-recognition API step to hit the sub-1-second mark. Copy and paste this right onto Upwork. It has every single feature, condition category, and protective barrier built in!

We are looking for an experienced API Integration Engineer to help finalize and optimize integrations for our security platform. The ideal candidate will have strong experience working with third-party APIs, authentication mechanisms, cloud-based AI services, and troubleshooting production integrations. Your primary responsibility will be to validate and configure API credentials for URL classification and IP reputation services, identify and integrate the correct Large Language Model (LLM) endpoint (OpenAI, Claude, Azure OpenAI, or custom/internal models), and ensure the overall system is secure, reliable, and high performing. This is a short-term contract with the potential for ongoing work if the engagement is successful. Responsibilities 1. Verify and configure API credentials for: - URL Classification services - IP Reputation services - Threat Intelligence APIs 2. Validate authentication methods including: - API Keys - OAuth 2.0 - Bearer Tokens - JWT 3. Identify the correct LLM provider and endpoint, including: - OpenAI - Claude (Anthropic) - Azure OpenAI - Google Gemini - Internal/custom LLM deployments 4. Confirm that all required API keys, secrets, and access tokens are correctly configured. 5. Test API connectivity and verify successful authentication. 6. Troubleshoot integration issues across development and production environments. 7. Optimize API performance, latency, retry mechanisms, and error handling. 8. Collaborate closely with our development team to resolve integration challenges. 9. Document the configuration process and provide recommendations for future maintenance. 10. Ensure best practices for credential management and secure secret storage. Required Skills 1. Strong experience integrating REST APIs 2. Experience with authentication protocols: - API Keys - OAuth2 - JWT - Bearer Tokens 3. Experience working with AI APIs including one or more of: - OpenAI - Anthropic Claude - Azure OpenAI - Google Gemini 4. Familiarity with URL reputation and threat intelligence services 5. Experience integrating IP reputation APIs 6. Strong debugging and troubleshooting skills 7. Knowledge of HTTP/HTTPS, JSON, webhooks, and API testing tools (Postman, Insomnia, etc.) 8. Experience with Python, Node.js, or similar backend technologies 9. Familiarity with cloud environments (AWS, Azure, or GCP) To Apply Please include the following in your proposal: - Brief overview of your experience with API integrations. - Examples of projects involving OpenAI, Claude, Azure OpenAI, or other LLM integrations. - Experience integrating URL classification, IP reputation, or cybersecurity APIs. - Your preferred development stack. We are looking for a highly skilled engineer who can quickly identify integration issues, ensure secure API connectivity, and help us deliver a robust, production-ready solution. If you have strong experience with API authentication, AI integrations, and troubleshooting complex systems, we'd love to hear from you.

Eligibility: This role is open to U.S. citizens only due to client security and compliance requirements. Please apply through this posting only — do not contact Data-Sleek directly regarding this position. Applications received outside this channel will not be considered and reported to Upwork. Data-Sleek is looking for a Senior AI Solutions Engineer to lead our on-premise and government-cloud AI deployments. You will design, build, and deploy AI-powered data pipelines for clients who cannot use commercial cloud due to ITAR, CMMC, or other data residency constraints, beginning with a client in the aerospace and defense sector. Beyond this first engagement, you will become Data-Sleek's go-to engineer for AI deployments across defense and aerospace clients, building the practice rather than just executing a single project. About Data-Sleek Founded in 2020, Data‑Sleek® is a U.S.-based AI and data consulting firm that helps mid-market companies build the data foundation that AI actually runs on. We own the full path — data strategy, architecture, integration, warehousing, and AI implementation — so organizations can adopt AI with confidence, stay compliant, and scale, without first hiring an internal data team. Our distributed U.S. team (San Francisco, Los Angeles, Irvine, Dallas, Chicago, and New York) partners with clients across healthcare, finance, insurance, logistics, and technology, modernizing data platforms with best-in-class tools like Snowflake, dbt, Fivetran, Tableau, and AWS. Trusted by Fortune 500 institutions and growing companies alike, Data‑Sleek turns complex data into measurable outcomes — faster insight, lower cost, and AI projects that deliver. About the Role You will own the technical delivery of AI-powered data pipelines in restricted environments where commercial cloud is not an option. The immediate engagement centers on a Product Lifecycle Management (PLM) data migration: building a pipeline that connects to a client's SharePoint on a restricted Microsoft 365 government tenant, reads engineering documents, classifies and summarizes them, detects duplicates, and rates naming-convention compliance to produce a migration-readiness report. You will start on-premise, then help the client evaluate and move to government cloud for production. Key Responsibilities AI Pipeline Development Build AI pipelines that connect to a client's SharePoint on a government cloud tenant, read engineering documents, classify them by type, generate summaries, detect duplicates, and rate naming-convention compliance in support of PLM data migration. Catalog large document repositories and produce migration-readiness reports and Excel catalogs that give clients a clear, measurable picture of their data. Engineer document-parsing workflows across DOCX, PDF, and XLSX formats, including embedding generation and database operations. On-Premise & Government Cloud Deployment Deploy on-premise first — a Mac Mini running Gemma via Ollama — standing up, serving, and tuning local inference infrastructure. Evaluate and migrate to production on Azure OpenAI (Azure Government) or AWS Bedrock (GovCloud) when the client is ready to scale. Keep deployments compliant within ITAR-sensitive, restricted-network boundaries throughout. Architecture & Cost Advisory Produce cost models and architecture recommendations that help client IT teams make informed platform decisions based on measured data, not vendor pitches. Compare deployment options — local, Azure Government, and AWS GovCloud — on cost, performance, and compliance, and explain the trade-offs clearly. Practice Building & Delivery Serve as Data-Sleek's go-to engineer for AI deployments across defense and aerospace clients. Build a reusable capability — a repeatable AI-solutions practice — rather than executing a single one-off project. What You Bring Required U.S. Citizen: U.S. citizenship is required and non-negotiable due to ITAR and client security and compliance requirements. Production LLM deployment: You have stood up inference infrastructure — not just called an API. You've handled model loading, memory constraints, failure modes, and throughput tuning in a real deployment. Local inference: Ollama, vLLM, llama.cpp, LM Studio, or TGI. You've served open-source models (Gemma, Llama, Mistral) on local hardware. Cloud AI platforms: Azure OpenAI or AWS Bedrock — at least one. Service configuration, model access, authentication, and token-based pricing. Python: Pipeline engineering — document parsing (DOCX, PDF, XLSX), API integrations, embedding generation, and database operations (SQLite, Postgres). Experience: 5+ years post-degree in software engineering, data engineering, or ML engineering. Strong Preferences Microsoft ecosystem: Entra ID, Microsoft Graph API, and SharePoint REST API at the API level. GCC High experience is a bonus. MCP (Model Context Protocol): Experience building or consuming MCP servers — a significant plus for a fast-evolving protocol. Workflow orchestration: n8n, Temporal, Airflow, or similar. The pipeline is orchestrated, not scripted. Government cloud awareness: Understanding of what FedRAMP High, IL4/IL5, and ITAR mean for cloud architecture decisions. Embeddings & vector similarity: sentence-transformers, pgvector, Qdrant, or FAISS for duplicate detection. 
Bonus (valued if present) Aerospace or defense experience: Familiarity with ECOs, BOMs, and AS9100 saves ramp time. Apple Silicon optimization: MLX, Metal acceleration, and Ollama tuning on M-series chips. Agentic frameworks: Bedrock AgentCore or Azure AI Foundry — the future direction involves agentic AI workflows on government cloud. What This Role Is Not Model training or fine-tuning. This is deployment engineering, not research. Data science or statistical modeling. The AI here is document understanding and classification, not predictive analytics. Frontend development. The deliverable is an Excel catalog and a report, not a web app. Sales or client acquisition. Data-Sleek's leadership manages the client relationship; you focus on delivery. Engagement & Compensation Remote, US-based. Occasional on-site travel to client facilities for hardware deployment and workshops may be needed. An average of 2–3 trips for the first engagement may be possible. Compensation. $40-$55/hour Why Join Data-Sleek? At Data-Sleek, you'll lead AI deployments in environments most engineers never touch — government cloud and on-premise systems where commercial tools simply aren't an option. Your work will directly shape how defense and aerospace clients adopt AI, and you'll build a reusable capability the company grows around. We focus on doing the right thing architecturally rather than selling the most expensive option, and we give our engineers the autonomy to deliver real solutions for real constraints. How to Apply If you've shipped real LLM deployments with real constraints, we'd like to hear from you. Please submit: Your resume A brief note describing one LLM deployment you've shipped — what model, what infrastructure, what data source, and what went wrong. Data-Sleek® is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all contractors.

We are a US-based sustainability technology company innovating smart city waste infrastructure through our new platform, Lashie. (Please note: Our Upwork profile is listed under our established corporate entity, Commit to Green). We have an existing, functional MVP architecture utilizing an ESP32 MCU and an HX711/Load Cell integration that successfully communicates with our cloud database and dashboard. We are now entering an optimization and hardening phase to resolve critical real-world reliability bottlenecks before evaluating this design for commercial manufacturing. We are seeking an independent, senior-level Embedded Systems / Firmware Engineer located in the United States (with a strong preference for candidates near Birmingham, AL for local hardware handoff) to join us for this tactical, short-term contract milestone. Key Responsibilities: •Resolve Wi-Fi Dropouts & Lockups: Analyze and overhaul the existing ESP32 network state machine. Implement robust, non-blocking retry logic and exponential backoff routines so the device seamlessly recovers from local network drops or heavy 2.4GHz RF congestion without freezing or draining power. •Fix Sensor Noise & Signal Drift: Isolate and mitigate electrical noise on the prototype (specifically diagnosing voltage spikes during high-current Wi-Fi transmission bursts). Implement clean software-level filtering algorithms (e.g., rolling median or moving averages) directly into the firmware to stabilize our HX711/load cell data acquisition. •Optimize Tare & Event Control Logic: Fix firmware state-machine bugs related to container swapping. Ensure the device executes stable auto-zero routines and accurately tracks net weight delta changes relative to a dynamically saved baseline, filtering out ambient vibration and negative feedback loops. •Implement Local Hardware Diagnostics: Re-map the onboard status LED to provide immediate, physical diagnostic blink codes (e.g., connection status, weight detection, or firmware exceptions) so field testing does not rely blindly on cloud telemetry availability. •Bench Testing: Utilize your own electronics lab diagnostic tools (oscilloscope, multimeter, power analyzer) to systematically stress-test physical prototype units. Requirements: •Location: Must be based in the United States. Direct preference for candidates in the greater Birmingham, AL area for frictionless hardware exchange. •Hardware Setup: If remote within the US, you must have your own bench setup and be willing to accept shipped physical prototype units, load cells, and calibration components. •Technical Expertise: Deep experience with C/C++ firmware development for the ESP32 ecosystem and analog-to-digital sensor data acquisition (specifically load cells/strain gauges). •Strictly Independent: No agencies or dev studios, please. We are looking to collaborate 1-on-1 with an experienced individual. Project Scope: Estimated 20 - 30 hours of dedicated troubleshooting and optimization. Success in this phase opens up long-term advisory and architecture opportunities as we progress toward factory production evaluation.

Azierta LLC is a U.S.-based engineering and infrastructure technical consultancy firm managing multi-state project pipelines and public works contracts. As we scale our active infrastructure operations, we are strategically building an elite network of Licensed Professional Engineers (P.E.) across multiple civil engineering sub-disciplines for ongoing and upcoming project assignments. We are seeking highly autonomous, senior-level engineers who can collaborate with us as independent subcontractors (1099 consultants) on a completely remote, flexible basis. No relocation is required. Core Sourcing Disciplines & Specialties: We are open to reviewing qualified P.E. profiles across all civil infrastructure sectors, with particular emphasis on: Traffic & Transportation Engineering: Highway capacity operational modeling, traffic impact assessments (TIA), network simulation, and corridor optimization (Synchro, VISSIM, HCS). Water Resources Engineering: Hydrologic and hydraulic (H&H) modeling, drainage design, stormwater management, and environmental compliance logs. Structural & Bridge Engineering: Comprehensive design reviews, structural calculations, and construction document production for bridges and highway infrastructure. Geotechnical Engineering: Foundation analysis, slope stability evaluation, soil mechanics reports, and technical site logs. Marine & Coastal Engineering: Ports, waterfront infrastructure, and coastal protection structures design. Key Responsibilities: Technical Quality Oversight (QA/QC): Perform meticulous peer reviews of engineering drawings, geometric layouts, computations, and design sheets. Review, Sign, and Seal: Act as the engineer of record on a project basis by legally signing and sealing final permit packages and construction documents ensuring full structural alignment with federal, local, and state DOT standards. Asynchronous Alignment: Participate in fast-paced alignment syncs and update milestone progress within our remote project tracking environments. Job Requirements: License Matrix (Mandatory): Must hold an active Professional Engineer (P.E.) License in good standing issued by a U.S. State Board. (Special priority for licenses in Louisiana, Maryland, and Washington D.C., but all active U.S. state licenses are highly valued). Experience Benchmark: Minimum of 10 years of active, hands-on experience within the U.S. infrastructure and civil engineering sectors. Autonomy: High responsiveness and availability to handle strict technical milestones on an on-demand basis. Compensation & Contract Structure: Project-Based Hourly Rate: This engagement operates under a pay-per-hour framework based on specific project hours and deliverables. We do not offer a fixed annual salary. Flexible Rate: There is no pre-established minimum or maximum rate. Hourly compensation is dynamic and will be negotiated per project based on task complexity, project budget, and individual P.E. qualifications