We're an early-stage AI startup building Hirey — an agent-to-agent marketplace that runs inside various AI tools via a plugin. Think "Upwork for AI agents": your agent finds, vets, and books the right human or agent on your behalf. We're looking for 5 developers to install our plugin (openclaw, codex, Opus, Gemini), try a sample workflow, and give us honest feedback on a 30-minute Zoom. About 30 minutes of your time total. What you'll do 1. Install the Hirey plugin in Codex. It connects your agent to Hirey’s remote MCP server, so there’s no local server, Node setup, Claude Desktop, or JSON config edit required. Setup is usually: enable the plugin, restart the AI agent you installed on 2. Connect to Hirey and run one sample workflow we send you. 3. 30-min Zoom with the founding team. We'll ask what confused you, what worked, what you'd change. Camera on, recorded. Who we're looking for - Someone who has used AI tools in the past, especially for any coding or technical tasks - You use Claude Desktop, Cursor, Codex, or similar AI dev tools regularly. - Bonus: you've built or contributed to anything in the AI agent / MCP / LangChain / Claude Code ecosystem. What you get - $25 flat, released via Upwork on call completion. - Early access to the Hirey AI agent network if you want to keep using it. - A direct line to the founding team — we genuinely want your criticism. To apply, answer these in your proposal 1. Have you used an AI coding tool before? Which one(s)? 2. One sentence on a recent AI/agent project you've worked on or played with. 3. Your timezone and earliest availability this week. We'll respond within 24 hours and schedule calls within 2 business days. No long applications, no portfolio review. Optimizing for speed.

We have a small Python-based machine learning inference service built with FastAPI and scikit-learn. The model was trained on structured tabular data, but our prediction endpoint is currently failing because of feature mismatch errors between the training pipeline and incoming API payloads. We need an experienced ML/MLOps engineer to quickly debug the issue, clean up the preprocessing logic, and make the `/predict` endpoint work reliably again. The goal is not to retrain the full model or build a large system. We only need a focused fix: review the existing model artifact, inspect the expected feature columns, update the API preprocessing code, and provide a short explanation of what was wrong. Bonus if you can also add a simple test request example or basic validation for missing fields. This should be a quick one-time task for someone comfortable with Python, scikit-learn, Pandas, FastAPI, and ML deployment workflows.

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.

i need an ai agent to answer the phones. find out what they need and get that info to us unless they need an estimate. if they need an estimate have them fill out the questionaire at popcornremoval.biz once that is done find out when is good for the estimate and set the appointment up in our software.

We're building a confidential, AI-native operating system for high-volume plaintiff-side litigation. This is not a generic legal chatbot. It's an operating system for litigation operations — and we already have a live law firm as the proving ground, a working visual prototype, a pitch deck, and a near-term demo deadline. We need a senior full-stack engineer who can take an existing prototype, schemas, prompts, and workflow package and turn it into a secure working demo, then a production-track MVP. The right person thinks like a product architect, engineer, and security operator at once — fast, but disciplined with confidential legal data. Required: React/Next.js, TypeScript, Node or Python/FastAPI, PostgreSQL, auth and role-based access control, OpenAI or comparable LLM APIs, structured JSON/schema outputs, secure file handling, PDF/export generation, GitHub workflows, and strong security discipline. Strong plus: Legaltech, plaintiff-side litigation, case management systems (Filevine, Litify, Clio, Salesforce, HighLevel), RAG/document extraction, audit logging, and SOC 2 / PII / regulated-data experience. Ground rules: NDA required. No public repos. No real client data in the demo — sanitized data only. No API keys in browser code. No external sharing or deployment without approval. First deliverable: A build-readiness report identifying what's mock, what's reusable, and what needs rebuilding, plus architecture, security risks, database plan, API integration path, and a 7–30 day build roadmap. The path: Paid 7-day build-readiness sprint → 30-day demo sprint → longer-term technical lead / founding engineer discussion. To apply, please include: A short note on why you're right for this project 2–3 relevant products you've built (links) GitHub or code samples, if available Your availability for a 7-day build-readiness sprint Your hourly rate, fixed sprint price, or contract-to-hire preference Remote acceptable. U.S.-based preferred; South Florida a plus.

Sphere Inc. is building a new AI-powered SaaS platform for the U.S. healthcare industry. We're looking for a senior engineer who enjoys building products from scratch, making technical decisions, and shipping production-quality software. This is an MVP that will quickly transition into production, so we're looking for someone who is comfortable owning architecture, development, deployment, and AI integration. We're developing a HIPAA-compliant Care Coordination Platform that helps physicians, nurses, and care coordinators manage chronic care patients more efficiently. A patient with diabetes and hypertension visits a primary care clinic. Instead of manually reviewing hundreds of pages of clinical notes, lab results, discharge summaries, and specialist referrals, the provider uploads the patient's records. The AI platform will: - Extract structured medical information from uploaded documents - Generate concise clinical summaries - Highlight medication conflicts and missing follow-ups - Detect abnormal lab trends - Recommend preventive care actions based on clinical guidelines - Generate visit notes and patient-friendly summaries - Allow physicians to approve, edit, or reject AI-generated recommendations - Maintain complete audit trails for HIPAA compliance The system must never expose PHI to unauthorized users and must meet healthcare security best practices. You'll work directly with our founders to design and build the MVP. Responsibilities include: - Design scalable backend architecture - Develop responsive React/Next.js frontend - Build secure REST APIs - Integrate OpenAI, Anthropic, or Azure OpenAI - Implement Retrieval-Augmented Generation (RAG) - Build document ingestion pipelines - Implement vector search - Build role-based access control - Design PostgreSQL database schema - Implement authentication and authorization - Deploy production infrastructure on AWS or Azure - Write automated tests - Optimize AI performance and costs Candidates should understand: - HIPAA Security Rule - PHI handling - Encryption at rest and in transit - Audit logging - Role-based permissions - Secure cloud architecture - Least-privilege access - Secrets management - BAA-aware cloud services Previous healthcare or medical SaaS experience is highly preferred. To Apply Please include the following in your proposal: - Links to recent AI SaaS or healthcare projects - Your GitHub profile - A brief description of your HIPAA or healthcare experience - 5–10 minute Loom video walkthrough of a HIPAA-compliant AI or SaaS project you personally built, highlighting the architecture, technical decisions, and your specific contributions.

I am seeking a software engineer with a strong interest engineering and linguistics to review some project files. The ideal candidate will have experience in software testing and technical writing, and be able to analyze and provide insights on pathways forward for the instantiation of the ideas.

We're building an internal AI system that runs entirely on our own hardware (no cloud inference) against our own company data. We have a working proof-of-concept and want to get the architecture right. We need an experienced consultant to review what we've built, pressure-test our decisions, and tell us where we're wrong. This is an advisory/validation role first — we have someone doing the hands-on work; what we want is a senior second opinion to make sure we're building this the right way. What we're running today: Inference: RTX 5090 (32GB, Blackwell), Ubuntu 24.04, running llama-server (llama.cpp + CUDA) serving Gemma 4 31B-it (Q4_K_M GGUF) at a 262,144 context window. Also hosts our MCP retrieval server, PostgreSQL, and Qdrant. Embeddings: separate machine with an RTX 3060 running vLLM serving Qwen3-Embedding-4B. RAG: hybrid retrieval — Postgres full-text search + Qdrant semantic search with RRF fusion, exposed through a custom MCP server with tool-calling. Data: ingesting our own internal operational data into Postgres + Qdrant. Planned stack: LiteLLM for model routing, n8n for automation, Open WebUI for the interface, Langfuse for observability, Vault or Infisical for secrets, Keycloak/Azure AD for SSO. What we need help with: Validating our two-machine split (inference vs. embeddings) and whether our VRAM/context budget holds up under real load — specifically whether a 256K context window is real and performant on a single 32GB card or just nominal. Model selection and routing strategy: which open-weight models for which tasks, and how to structure LiteLLM routes. RAG quality: chunking, embedding dimensionality, hybrid search tuning, reranking — making retrieval actually accurate on messy real-world data. Sanity-checking our overall architecture and telling us our blind spots. You should have done: Stood up local LLM inference in production — llama.cpp/llama-server and vLLM, not just Ollama on a laptop. You understand GGUF quantization (Q4_K_M, IQ-series), KV cache, KV-cache quantization, and how context length maps to actual VRAM consumption. Real fluency in GPU sizing math — given a model, a quant, and a context window, you can tell us whether it fits on a given card and what throughput to expect. Bonus if you've worked with Blackwell / sm_120a. Built production RAG — vector DBs (Qdrant, pgvector), hybrid search, RRF fusion, embedding model selection, reranking, evaluation. Worked with agentic/tool-calling systems and ideally MCP servers. Know the open-weight model landscape (Gemma, Qwen, Llama, Mistral, Phi, Nemotron, Hermes) and their licenses well enough to advise. Production ops: systemd, Docker, model gateways (LiteLLM or similar), observability (Langfuse), secrets management, SSO.

I'm currently using Claude, Google Workspace, App Script and Lambda on AWS to pull data out of an operation software called Fullbay to automate a bunch of different aspects of our business. I'm a former software engineer, but now own a medium sized business in Denver and I want someone to help me continue these efforts. For the most part, these efforts consist of many, but small projects that pull data out of the ops software, push it into a Sql server db, and then compare data across google chat, google email, workspace calendar, hubspot, and fullbay to find specific issues with our staff and workflows and notify and track and report. A lot of cool, quick projects that I need help with. I would strongly prefer someone that lives near commerce city colorado so that they can get to know the business.

I'm an independent inventor (Massachusetts LLC, patent-pending) developing a portable sports-training device that uses a projected laser line and a global-shutter mono camera to measure the angle of a small metal striking surface at the moment of impact. Target accuracy is ±0.5° on the angular measurement, with measurement latency under 2 seconds, and direct-sunlight robustness as a key engineering risk. This is an end-to-end Phase-0 feasibility engagement. The working assumption is laser-line + global-shutter mono camera with bandpass filtering, but I want your read on whether that's the right approach for this accuracy and these conditions. I have a strong lean, not a closed decision, and I'd rather you push back early than build something the wrong way. Once we align on the approach, you'll spec the bench rig (camera model, laser modules, filters, optics, baseline geometry, target mounting); I'll source the parts from your BOM and either ship the components for you to assemble or assemble and ship a built rig, your preference, whichever fits your workflow best. From there you capture data under controlled and outdoor conditions, develop the detection and calibration pipeline, and deliver a working codebase plus a written accuracy/robustness report. Hardware is returned to me on completion (or retained for a follow-on engagement if we both want to continue). What you'll deliver: 0. Approach review + rig spec. A short written deliverable (2–4 pages) covering: (a) your read on the proposed sensing approach, affirm + refine, or argue for an alternative with reasoning and a specific recommendation; (b) a bench-rig BOM with specific parts (camera model, laser modules, bandpass filters, optics, mounting, target plate) sized for the working distance and accuracy spec; (c) laser-to-camera baseline geometry with your reasoning, and recommended calibration targets. I'll source the parts from your BOM. We'll decide together whether I ship components for you to assemble or assemble and ship a built rig, whichever you'd rather. 1. Rig assembly or acceptance + baseline capture. Receive shipped parts (or built rig), assemble or validate alignment as appropriate, confirm basic optical performance against the M0 spec, then capture a baseline dataset (~200 frames per configuration) under controlled indoor lighting. Photos of the as-built rig and a setup diagram included. 2. Detection pipeline. A Python/OpenCV module that extracts the projected laser line with sub-pixel accuracy from frames at 60–100 fps. Sub-pixel line fit (Steger, Gaussian, parabolic) or weighted centroid, your choice with a short justification. 3. Calibration framework. Documented procedure and accompanying script for mapping pixel displacement to angular displacement of the target plate, accounting for camera intrinsics, lens distortion, and laser-to-camera baseline geometry. Validation against ground-truth rig angles. 4. Robustness data capture + analysis. Re-capture under (a) bright indoor with mixed daylight and (b) direct outdoor sunlight, for both laser variants with and without matched bandpass filters. Quantified accuracy + jitter per condition. 4–8 page PDF report comparing visible-red + bandpass vs. near-IR + matched bandpass. 5. Stretch (optional milestone): First cut at deriving angle-at-impact from a short pre/post-impact image sequence, pseudocode or working prototype, whichever fits the time budget. Deliverable format: Well-commented Python module(s) in a Git repo I'll provide, a README that walks a junior engineer through running the pipeline end-to-end, the captured datasets (raw frames + ground-truth angles), and a PDF report. What I'm looking for: - Comfort giving an unambiguous engineering recommendation: "use this approach with these parts" or "don't and here's why, and here's what to do instead." Phase 0 succeeds or fails based on the judgment in Milestone 0 as much as the algorithm in later milestones. - 5+ years of practical computer vision work, with shipped projects involving line/edge detection, sub-pixel feature localization, or structured-light triangulation. - Comfort doing your own benchtop work; mounting, alignment, basic optics handling. - Strong Python + OpenCV; comfort with NumPy/SciPy for the line-fit and calibration math. - Camera calibration experience (OpenCV calibrateCamera, distortion coefficients, projective geometry). - A workspace where you can run an outdoor sunlight test safely and legally with a Class-2 visible-red laser and a Class-1 IR laser module. - Bonus: prior work with laser triangulation, structured-light scanning, or sports/motion-tracking applications. - Bonus: experience deploying CV pipelines to Raspberry Pi or ESP32-S3-class hardware (potential follow-on scope). Engagement: - Fixed-price (preferred): $5,000–$7,500 total, paid across 5 milestones (approach review + rig spec → baseline capture → detection pipeline → calibration → robustness report). - Hourly alternative: $70–$140/hr with a 75-hour cap, then re-scope. - Duration: 5–7 calendar weeks (approach-review phase happens up front; ~1 week round-trip shipping after rig build). - Weekly 30-min check-ins (US Eastern preferred; flexible). - Hardware: shipped to you fully insured at my cost. Returned (insured, my prepaid label) on completion, or retained for follow-on engagement. - Possible follow-on: porting the pipeline to Raspberry Pi / ESP32-S3, IR laser variant tuning, integration support for the next prototype phase. Before we start: Short NDA + IP assignment signed before I ship the kit, share the technical design doc, or grant repo access. Upwork's standard terms transfer IP on payment, but I want a standalone signed PIIA on file as well, routine, less than 1 hour of your time. To apply, please include: 1. 1–2 examples of prior CV work involving sub-pixel localization, line fitting, or laser/structured-light triangulation. Paragraph + GitHub or paper link. 2. Three or four sentences on your approach to extracting a sub-pixel laser line centroid from a single frame. 3. Confirm you have a workspace where you can run both indoor and outdoor (direct-sunlight) image captures with a small bench rig, and that you're comfortable assembling the shipped kit. 4. Whether you prefer fixed-price or hourly, and your proposed milestone breakdown. 5. Without committing to a final answer until you've seen the full spec, a quick take: do you think projected laser line + global-shutter mono camera is the right sensing approach for ±0.5° angular accuracy at 60–100 fps under direct sunlight, or would you steer me toward a different approach? Two or three sentences. Looking forward to talking with strong candidates. Jason