I need help completing this project today. The project sits between data engineering and AI. I have source data that needs to be cleaned, structured, and prepared so it can support both analytics and RAG-style AI workflows. The goal is to create a reliable pipeline that takes raw data, normalizes it, preserves basic metadata/lineage, and outputs clean datasets that can be used for dashboards, vector indexing, or internal AI tools. The work may include: - Reviewing the current source data and structure - Cleaning and normalizing datasets - Designing or improving an ETL/ELT flow - Preparing AI-ready data for RAG or vector search - Adding basic validation checks - Organizing outputs for analytics use - Documenting the final workflow clearly Ideal freelancer has experience with: - Python and SQL - Data engineering / ETL pipelines - Databricks, Spark, or similar tools - RAG data preparation - Data cleaning, validation, and modeling - Cloud data storage such as S3, Postgres, or similar This is urgent and must be completed today. Please only apply if you are available immediately and can work quickly without a lot of hand-holding. When applying, please include: - Your relevant experience with AI-ready data pipelines or RAG data preparation - The tools you would use - Confirmation that you can complete this today - Your estimated timeline for delivery

I am seeking someone to help me set up my social media calendar and guide me in learning how to use it effectively. The task involves both technical setup and educational guidance to ensure I can manage my calendar independently. The ideal candidate should have experience in social media with Instagram, Amazon stores, etc.

Remote Full Stack Developer (U.S. Only) | Up to $50/hour We are hiring a Remote Full Stack Developer for a long-term opportunity with our U.S.-based partner. We're looking for developers who are passionate about building scalable web applications and enjoy working in a collaborative remote environment. Requirements Must be based in the United States (U.S. Citizens or Permanent Residents preferred) 3+ years of Full Stack Development experience Strong experience with React, Next.js, Node.js, TypeScript, and JavaScript Experience with APIs, databases (PostgreSQL, MongoDB), and cloud platforms Strong communication skills and the ability to work independently Nice to Have Experience with AWS, Docker, and CI/CD Python or C# experience Startup experience What We Offer 💻 100% Remote 💰 Up to $50/hour (depending on experience) 📈 Long-term opportunity 🤝 Flexible working environment How to Apply Please include: Resume Portfolio or GitHub LinkedIn Profile Expected hourly rate Confirmation that you are currently residing in the United States We're hiring immediately and will begin interviewing qualified candidates right away.

We are looking for a Senior Software Developer with excellent technical skills and outstanding interview abilities. The ideal candidate should be able to communicate clearly, think through problems in real time, and perform exceptionally well in technical discussions, coding interviews, and system design sessions. Requirements - 8+ years of professional software development experience - Strong expertise in one or more technologies such as Python, Node.js, Java, .NET, React, or similar - Solid understanding of system design, APIs, databases, cloud platforms, and distributed systems - Excellent problem-solving and analytical skills - Exceptional verbal and written English communication - Ability to explain technical concepts clearly and confidently - Strong performance in live coding, debugging, and system design interviews - Professional attitude and ability to think under pressure Responsibilities - Design, develop, and maintain scalable software solutions - Collaborate with engineering and product teams - Participate in architecture discussions and technical decision-making - Write clean, maintainable, and high-quality code - Mentor team members and contribute to best engineering practices Ideal Candidate - Performs exceptionally well in technical interviews and coding assessments. - Communicates ideas clearly and confidently. - Can discuss past projects in depth and explain technical decisions. - Demonstrates strong ownership and leadership qualities.

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.

We are seeking a Machine Learning Engineer with a strong background in Computer Vision and ML fundamentals. The ideal candidate will have experience in healthcare and automotive domains. This role requires someone based in the US, with the ability to work for 6+ months. The candidate should be able to integrate into our team seamlessly and contribute to ongoing projects effectively. [IMPORTANT] In order to verify your language preference. please attach your 1 or 2 mins intro video.

First things first: as the title suggests the compensation for this project is revenue based generated by new subscribers. That said here is the breakdown; A-Deployment of platform from repository to infrastructure ready-to-go live. Compensation $350 B- “On-call developer” in the event the platform goes down ( very unlikely). Reply to tickets submitted to through support. Compensation $250/mo C-A $500 bonus once 50 subscribers are reached. Most of the development has already been created, tested and completed a finished code-platform has been delivered and pushed to GitHub repository, but it is not yet deployed to the site (myfelipe.ai which still runs an older version). The plan is to follow these steps in order to deploy the code to the servers and prepare the platform to go live. Step 1—follow steps in the Pre-launch file attached. Step 2-Coordinate an online meeting to validate for Stripe account access code. Eventually get an invite as a master-developer. Step3-Push the code to Render/Vercel and set the production config (NODE_ENV, Stripe webhook secrets). Step-4 After the platform has gone live, a test of all the features must be performed, once confirmed everything is properly tested. Our waitlist promotion can be deployed (this has already been created and tested) We hope to launch July 24th but before the launch we should have collected some waitlist memberships before launch. Pls. Check the URL myfelipe.ai there you will find the 3 tears for members. We want to give members an additional discount if they sign up for 2 years instead of a monthly subscription. Also we would like to add a 5-year and a lifetime membership at significant discounts. Those who pay the $99 dollars upfront to be on the waitlist are eligible to receive an exclusive pricing. We believe in this exciting project hope you can catch the vision of helping small businesses be more efficient though ai. Come and join us.

I am looking for a reliable freelancer to perform a quick, daily remote check on a Windows/Mac laptop to ensure it remains online, stable, and running properly while I am traveling. This is a 100% remote digital task. Responsibilities: - Log in remotely once per day (via AnyDesk/TeamViewer) at a mutually agreed time. - Verify the internet connection speed and system stability. - Run a quick, specific test script/application (instructions will be provided). - Send a daily brief report or screenshot confirming the system status. Requirements: - Experience with remote desktop tools and basic system monitoring. - Excellent punctuality and daily availability. - Must track time or milestones transparently through Upwork. - Must sign a standard non-disclosure/confidentiality agreement.

We are academic researchers studying how software engineering work is organized and how it has changed over time, particularly in response to generative AI tools. We are looking to interview several software engineers about their professional experience. Any information you provide will be used as part of academic research outputs, and will not be attributed to your name or your company. Compensation: $30 fixed rate (i.e., $60/hour for a 30-minute Zoom interview) --- Who we're looking for - Currently working or recently worked (within the past 6 months) as a full-time software engineer at a company. Since we are interested in learning about your usage of AI within a software engineering team, we cannot accept individuals who do not have this form of recent experience. - Any level of seniority welcome What the interview covers - How engineering work is organized at your company (or past company) - How workflows have changed in response to AI - Your experience with tools and workflows Output - The qualitative interviews will be used to inform research hypotheses for the academic project - With permission, we would also include the information directly in the qualitative section of an academic paper, covering interviews with 30+ tech workers --- e.g., as paraphrased or anonymized quotes --- To apply, please briefly describe: 1. Your experience in software engineering roles, over the past 4 years -- company names, job titles held, and dates of positions held 2. Your availability for a 30-minute video call interview Thank you!

DESCRIPTION; I'm building a data infrastructure product for ontology-driven AI context: object types, properties, and relationships materialized ahead of query time, so AI systems retrieve connected context fast instead of rebuilding it from raw sources on every request. I need experienced eyes on the ingestion foundation before anything gets built on top of it. The deliverables are fixed (below); hours are flexible — propose what you think the work honestly takes. Rate: my budget is $50–75/hr. That's a hard ceiling — proposals above that range can't be afforded and won't be considered, regardless of quality __________________________________________________________________________ WHO SHOULD APPLY A data engineer / data infrastructure engineer who understands what an ontology and a knowledge graph are and why they matter for AI systems — connected entities and relationships as first-class context, not just tables. You don't need graph database experience; you need to get why pre-materialized, relationship-aware data beats rebuilding context from raw sources on every query. If that framing clicks for you, you're the right kind of applicant. __________________________________________________________________________ THE PRODUCT, HIGH LEVEL: The platform deploys on a client's own infrastructure — we never see their data. Clients connect their data sources, define an ontology (object types, properties, relationships), and the platform materializes it across tiered storage. Later phases add a binary serve layer, SSD/RAM caching, and GPU-parallel query execution so AI systems and data applications retrieve connected context at very low latency. Target customers: companies running AI on complex connected data (security operations, healthcare, financial services) where privacy demands private deployment and speed matters. Storage note: the current prototype uses Iceberg on GCS for development convenience, but the architecture is intentionally built for any S3-compatible storage (on-prem S3, private cloud VPC, MinIO, etc.). Portability is a design requirement, not an afterthought — the platform must never be tied to a single cloud provider. __________________________________________________________________________ WHAT EXISTS TODAY: A working Python prototype: FastAPI, PyIceberg, PyArrow, Postgres, Supabase (metadata + sync ledger), GCS as the Iceberg warehouse. Architecture and design docs are provided for orientation. The cold path is functional and tested: a 31-test production suite ran against live infrastructure at 1M–5M row scale — core correctness, concurrency, failure injection (kill mid-sync, storage outages, lease expiry), idempotency/replay, rollback, a 50-sync soak, and audit checks. All passing, with a written sign-off document you'll receive. That's exactly why I'm hiring you: tests confirm behavior I anticipated. You're here for what I didn't anticipate — structural weaknesses, hidden risks, and edge cases that a test suite written by the same mind that wrote the pipeline can't catch. I'm strong on product and systems design, not low-level data engineering. The codebase is AI-assisted, and I want a professional to find what that typically accumulates. This is a prototype built from the ground up — no live client today. The goal: ensure the ingestion foundation is genuinely solid (data coming in from source correctly, at scale, repeatedly) so a scoped MVP pilot and beta release won't break under real usage. You are validating the foundation before anything gets built on top. __________________________________________________________________________ YOUR SCOPE — THE COLD PATH, END TO END Data source → validation → identity merge → materialized ontology in Iceberg on S3-compatible storage. The data connectors are in scope — they ARE Milestone 1. The platform supports exactly three ways data comes in, and your job includes confirming each one is genuinely production-grade, not just demo-grade: Postgres — full refresh and incremental watermark sync S3-compatible object storage (CSV) — currently GCS via S3 interop, but must work against any S3-compatible store (on-prem, MinIO, private VPC) Manual CSV upload — primarily for testing/onboarding For each connector, production-grade means: real error handling (bad credentials, unreachable source, permission failures, malformed/garbage data, schema drift), clear failure messages that tell a user what broke, no silent partial ingests, and sane retry/recovery behavior. If a connector swallows errors, loses rows quietly, or fails confusingly — that's exactly the finding I'm paying for. No other connectors are planned for this milestone. Three connectors that work correctly under stress beats ten that mostly work. Focus areas across the pipeline: Connectors — production-readiness and error handling as described above Identity & matching — entities staying consistent across syncs (PK merge, fingerprint mode, composite keys) Sync semantics — full refresh vs incremental watermark sync, replay idempotency, delete behavior Relationships — FK→PK edge materialization, rebuild triggers, orphan handling, stable node identity Versioning & audit — Iceberg snapshots, rollback, schema change lineage, sync ledger completeness Reliability — failure modes, partial writes, lock/lease behavior, silent wrong-data risks Code structure — dead code, duplication, coupling, fragility; source-specific logic must stay contained in each connector and never leak into the shared pipeline Explicitly out of scope: GPU execution, query kernels, binary serve formats, caching layers, query-time serving, and any new connector types — all future phases. Your scope ends at correct, versioned, audited data in Iceberg. __________________________________________________________________________ DELIVERABLES (in priority order) Prioritized written assessment — what's pilot-ready as-is, what must be fixed before a real pilot customer (with specific recommendations), and what the existing test suite missed (edge cases, risks, gaps). Active code changes — implement fixes for the highest-priority issues you find, directly in the repo. You'll have full repo access. I'm open to architecture changes and refinements as long as they're clearly explained with reasoning. A change log that teaches — for every change: what you changed, why it mattered, what it fixes or prevents, and what to watch for going forward. This isn't paperwork — I'm making a local engineering hire for the next milestone, and your write-ups become the onboarding record. Everyone who touches this codebase after you should learn from what you found. Fixes go deepest-risk-first. What you get from me: repo access, architecture/design docs, the test suite + sign-off report, and async availability for questions. __________________________________________________________________________ ***REQUIRED EXPERIENCE: 1)Production Python data pipelines 2)Apache Iceberg, Delta Lake, or Hudi (or strong Parquet/data-lake work) 3)Postgres 4)Merge/upsert, idempotency, watermark/CDC patterns Building or hardening data connectors that real users depend on************* __________________________________________________________________________ WHERE THIS CAN GO: This starts as a fixed-scope review. Separately, I plan to make my first part-time/full-time engineering hire locally (Dallas) to build Milestone 2 and beyond — SSD caching, serve layers, containerization, and microservices as the platform scales. For the right freelancer, there's opportunity to stay engaged on recurring scoped work — reviewing the foundation as it evolves and working in conjunction with that future hire. Not required, not promised — but the door is open if the work is strong. __________________________________________________________________________ *********HOW TO APPLY — READ CAREFULLY***** Answer this one question in your proposal, briefly and in your own words: "You're building a pipeline that ingests from Postgres and S3-compatible storage and materializes a connected ontology (entities + relationships) into Iceberg. How do you design the sync process to be reliable and idempotent — especially around watermarking, commits, and failure handling between steps?" Include your proposed hour estimate for the deliverables above. Get creative — attachments and notes welcome. Note on AI-generated proposals: I use AI heavily myself — but if your proposal or screening answer is clearly AI-generated boilerplate, you will be automatically rejected without consideration. I'm hiring your judgment and experience, not your ability to paste a prompt. Short, direct, human answers. __________________________________________________________________________ A NOTE ON TECHNOLOGY BOUNDARIES: ***QUICK EXAMPLE*** FastAPI and Iceberg are what the platform uses today, not permanent decisions. As the product scales, we may want to run FastAPI alongside a second framework, replace it entirely, or eventually move away from Iceberg toward a custom storage format optimized for the GPU serve layer. Those should be engineering decisions made on merit, not decisions we're forced into because the current code makes swapping painful. What I need confirmed: is the codebase modular enough that a change like that stays contained? Core business logic (validate, merge, materialize, version) should never be tangled directly with infrastructure. API routes should be thin entry points that hand off to service logic, not where business logic lives. Iceberg writes should be isolated behind a single abstraction. If those boundaries are clean, replacing or extending a technology layer is a focused engineering effort. If they're not, it touches everything and becomes a mess under deadline pressure with a full team. Flag anywhere that boundary is broken. That's a priority finding. __________________________________________________________________________ FINAL REMARKS: NDA & IP protections This engagement requires signing an NDA and IP assignments agreement before work begins; standard protections given you'll have full repo access to a pre-launch product. Documents are provided on day one; nothing unusual in them. If that's a dealbreaker, please don't apply.