We are seeking a skilled computer vision and AR engineer to develop a prototype for our project. The ideal candidate will have experience in building prototypes and a strong understanding of computer vision and AR technologies. You will be responsible for designing and implementing the prototype, ensuring it meets our project requirements. If you have a passion for innovation and a keen eye for detail, we would love to hear from you.

I’m looking for a developer to help build a lightweight AI prototype using OpenAI or Anthropic APIs. This is NOT a full product build. This is a focused prototype to test a specific idea. Project Goal: Build a simple Python-based system that: Runs the same LLM task multiple times. Captures outputs and any intermediate state (memory/logs). Compares differences between runs. Classifies differences into simple categories: Stable Boundary Violation What This Means Think: •Run the same prompt 5–10 times. •Log results. •Detect where outputs or stored data differ. •Label those differences. That is it. Technical Requirements Must have: •Python •Experience with OpenAI API or Anthropic API •Ability to build simple, clean scripts (no over-engineering) Nice to have: •LangChain or similar frameworks. •Streamlit (for simple UI/dashboard). •Experience with logging or comparing outputs. Important Constraints This should be: •Lightweight. •fast to build. •easy to understand. Please DO NOT: •Design complex architectures. •build full systems. •over-engineer. Deliverables •Python script or small app. •Ability to run repeated LLM tasks. •Stored logs of runs (JSON or similar). •Basic comparison logic between runs. •Simple classification output. Timeline •3–7 days initial build •Max 1–2 weeks total Engagement Style •Fixed-price or hourly (open to discussion) •Will start with a small paid test task before full project Screening Question (Required) Please answer this: If you needed to run the same LLM task multiple times and compare outputs/state between runs, how would you build it quickly? Who This Is For Ideal candidate: •Builds fast prototypes. •Comfortable with LLM APIs. •Prefers simple solutions over complex systems.

We're building an AI Research Copilot for our financial analytics platform. The Copilot will help investors and financial professionals analyze market data, company financials, news, and earnings using natural language. This is a production AI project, not a basic chatbot. Responsibilities Build a production-ready AI Research Copilot Design and implement RAG pipelines Integrate LLMs with our financial data and APIs Implement semantic search and tool calling Build conversation memory and streaming responses Add source citations and improve response accuracy Optimize performance and reduce hallucinations Required Skills Python OpenAI, Claude, or Gemini APIs RAG and vector databases (Pinecone, Qdrant, pgvector, Weaviate, etc.) LangGraph, LangChain, LlamaIndex, or similar frameworks API integration Prompt engineering Production AI application experience Please include: AI products or copilots you've built Your experience with production RAG systems Your preferred AI architecture for this project Briefly explain how you reduce hallucinations and provide trustworthy AI responses. We're looking for an experienced engineer who can build scalable, production-quality AI systems and collaborate long term.

We are seeking a skilled computer vision and AR engineer to develop a prototype for our project. The ideal candidate will have experience in building prototypes and a strong understanding of computer vision and AR technologies. You will be responsible for designing and implementing the prototype, ensuring it meets our project requirements. If you have a passion for innovation and a keen eye for detail, we would love to hear from you.

We're a US-based software consulting firm looking for a skilled full-stack engineer to join us long-term. This starts as an hourly engagement with a paid trial project, and converts to full-time for the right person. We have consistent client work and need someone reliable who ships fast and communicates well. This is not a vibe-coding role. We want a real engineer — strong fundamentals, solid architecture instincts, clean production code — who uses AI tools (Claude Code, Codex, Cursor) to move 5–10x faster. AI is your force multiplier, not your crutch. If you lean on AI to paper over gaps in your actual engineering ability, this isn't the fit. If you're a genuinely strong developer who has mastered AI-augmented workflows to ship more and better, keep reading. What you'll do: Build and ship full-stack web apps, APIs, integrations, and backend systems for our clients Own projects end-to-end: architecture, implementation, testing, deployment Juggle multiple client projects at once (AI leverage makes this realistic) Communicate clearly and proactively — updates, blockers, timelines Requirements: US-based. This is a hard requirement — please do not apply if you're not based in the United States. Native or fluent English, excellent written and verbal communication 4+ years professional software engineering experience Strong across a modern stack (examples: TypeScript/React/Next.js, Node, Python, Postgres, cloud — AWS/GCP) Genuine architecture and system-design skills, not just feature-wiring Fluent with AI-assisted development (Claude Code, Codex, Cursor) and able to speak to how you use it to ship faster without sacrificing quality Self-directed, reliable, and able to own work without hand-holding Nice to have: Experience across multiple client projects or agency/consulting background AI/ML integration experience (LLM APIs, RAG, agentic workflows) DevOps / secure cloud deployment experience How we work: Start: paid hourly trial on a real project so we can both evaluate fit Then: ongoing hourly, scaling toward full-time (40 hrs/week) Long-term, stable relationship — we're building a team, not filling a one-off gig To apply, include: A short note on your engineering background and your strongest projects How you specifically use AI tools in your workflow — be concrete, tell us your setup and where it makes you faster Links to work (GitHub, portfolio, shipped products) Your hourly rate Please start your application with the word "SHIPPED" so we know you read this in full. Applications without it will be ignored.

The objective is to develop an AI model that detects and classifies Longitudinal and Transverse Cracking (LTC) in asphalt airfield pavements, in accordance with ASTM D5340-24. The model will use fused Digital Elevation Model (DEM) and imagery data to identify cracks, measure their extent, and assign a severity level (Low, Medium, or High) based on the standard's width and spalling criteria. The output will support the broader Pavement Condition Index (PCI) workflow used across the company's 85-model system. What you'll need to do: • Access and understand the provided DEM and imagery data, along with existing labels • Build a pipeline that fuses DEM and imagery to detect cracks and measure their length and width • Train a model to classify each detected crack as Low, Medium, or High severity per the ASTM thresholds • Apply the standard's exclusion rule so distresses are not double-counted where other cracking is already recorded • Validate the model's outputs against the team's verified ground truth and report accuracy • Deliver the trained model along with a short write-up of results and any limitations

Building a machine learning platform for an insurance related product, with a focus on data pipelines, feature and label design, model development, deployment planning, monitoring, and business impact. Looking for an experienced MLOps or applied ML engineer to provide weekly mentorship through structured project check ins. The implementation will remain entirely my responsibility. Each meeting will focus on the current state of the project. I will provide detailed context on what has been completed, the decisions being considered, current blockers, and the next stage of work. The mentor will review that specific situation, challenge assumptions, identify gaps, and provide direct feedback based on how the issue would be handled in a real production environment. The goal is not general instruction. Feedback should be specific to the project, its architecture, data, constraints, and business use case. Meeting Structure: - Approximately 60 minutes per week - Review of progress since the previous meeting - Discussion of current technical and business decisions - Review of architecture, pipelines, model design, or deployment planning - Identification of risks, missing requirements, and unnecessary complexity - Clear recommendations and next steps for me to complete independently Scope of Guidance: - Business use case and ROI analysis - Data architecture, quality, lineage, and source contracts - Record grain, joins, and entity matching - Feature engineering and leakage prevention - Label and outcome design - Model evaluation and business metrics - Experiment and dataset versioning - Batch and real time deployment - Monitoring, drift, retraining, and rollback - Reliability, privacy, security, and governance Work Expectations: This is a meeting based mentorship role only. The mentor will not be expected to: - perform implementation work outside scheduled meetings - write or maintain the codebase - prepare separate reports or deliverables between meetings - manage the project - provide ongoing asynchronous support - take ownership of delivery Required Experience: - Professional experience building or operating production ML systems - Strong understanding of MLOps, data engineering, deployment, and monitoring - Experience reviewing real technical systems and making practical recommendations - Ability to explain tradeoffs clearly and give direct, specific feedback - Willingness to challenge weak decisions rather than provide generic advice Working Style: Direct communication and practical feedback are important. I will prepare the project context and questions before each meeting, complete the work independently afterward, and return with results for review. Initial Engagement: The engagement will begin with one paid consultation. The session will be used to review the project, discuss the expected mentorship style, and determine whether recurring weekly meetings are a good fit.

I'm early in my career and self-taught, working on breaking into AI engineering, and I want one honest hour with someone actually in the trenches — not a sales pitch, not encouragement. Just a candid conversation, video on. What I'm after: your unvarnished read on how someone in my position actually breaks in, where the field is really headed, what companies are truly paying to have built or solved, and — if you were starting out today with what you know now — exactly what you'd do over the next 6 months. About me: no CS degree, no traditional pedigree — I've been learning by shipping real projects (I've built a couple of small live AI products end-to-end: RAG, an agent, automation). I know enough to ask good questions and I'll come prepared so we use the time well. I'd also love to show you one thing I built for 60 seconds of brutally honest feedback.

Building a machine learning platform for an insurance related product, with a focus on data pipelines, feature and label design, model development, deployment planning, monitoring, and business impact. Looking for an experienced MLOps or applied ML engineer to provide weekly mentorship through structured project check ins. The implementation will remain entirely my responsibility. Each meeting will focus on the current state of the project. I will provide detailed context on what has been completed, the decisions being considered, current blockers, and the next stage of work. The mentor will review that specific situation, challenge assumptions, identify gaps, and provide direct feedback based on how the issue would be handled in a real production environment. The goal is not general instruction. Feedback should be specific to the project, its architecture, data, constraints, and business use case. Meeting Structure: - Approximately 60 minutes per week - Review of progress since the previous meeting - Discussion of current technical and business decisions - Review of architecture, pipelines, model design, or deployment planning - Identification of risks, missing requirements, and unnecessary complexity - Clear recommendations and next steps for me to complete independently Scope of Guidance: - Business use case and ROI analysis - Data architecture, quality, lineage, and source contracts - Record grain, joins, and entity matching - Feature engineering and leakage prevention - Label and outcome design - Model evaluation and business metrics - Experiment and dataset versioning - Batch and real time deployment - Monitoring, drift, retraining, and rollback - Reliability, privacy, security, and governance Work Expectations: This is a meeting based mentorship role only. The mentor will not be expected to: - perform implementation work outside scheduled meetings - write or maintain the codebase - prepare separate reports or deliverables between meetings - manage the project - provide ongoing asynchronous support - take ownership of delivery Required Experience: - Professional experience building or operating production ML systems - Strong understanding of MLOps, data engineering, deployment, and monitoring - Experience reviewing real technical systems and making practical recommendations - Ability to explain tradeoffs clearly and give direct, specific feedback - Willingness to challenge weak decisions rather than provide generic advice Working Style: Direct communication and practical feedback are important. I will prepare the project context and questions before each meeting, complete the work independently afterward, and return with results for review. Initial Engagement: The engagement will begin with one paid consultation. The session will be used to review the project, discuss the expected mentorship style, and determine whether recurring weekly meetings are a good fit.

We're hiring a senior AI developer to build and deploy AI solutions for a fintech/credit-union platform. The work spans autonomous banking agents, fraud detection, credit scoring, and bill-pay/invoice automation — at the intersection of LLMs, cloud infrastructure, and financial-domain expertise, with security and compliance built in from the start. This is a long-term, ongoing engagement. What you'll do: AI agents & orchestration - Design, build, and deploy multi-agent systems using Amazon Bedrock Agents, LangChain, and related frameworks - Architect agentic workflows for core banking use cases: credit scoring, fraud detection, bill-pay automation, invoice management - Define agent personas, memory strategies, tool-use patterns, and escalation paths for production banking agents LLM engineering - Fine-tune, prompt-engineer, and evaluate LLMs for financial-domain tasks - Build RAG pipelines over credit-union knowledge bases, policy docs, and member data - Implement guardrails, content filtering, and compliance checks for safe, regulated outputs - Monitor performance, hallucination rates, and latency against SLAs Cloud infrastructure (AWS & Azure) - Architect and manage AI/ML workloads on AWS (Bedrock, SageMaker, Lambda, S3, IAM, VPC) and Azure (OpenAI Service, Azure ML, AKS) - Design secure, cost-optimized environments compliant with NCUA, PCI-DSS, and SOC 2 - Implement infrastructure-as-code with Terraform or AWS CDK DevOps & MLOps - Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, CodePipeline, Azure DevOps) - Containerize services with Docker, orchestrate with Kubernetes (EKS/AKS) - Apply MLOps best practices: model versioning, A/B testing, canary deployments, automated rollback - Stand up observability with logging, tracing, and alerting Python development - Write clean, well-tested Python for AI pipelines, REST APIs, and data workflows - Build FastAPI/Flask microservices exposing agent capabilities to frontend and core banking systems - Integrate with financial data sources, core banking APIs, and third-party fintech services Banking applications - Build credit-scoring models using alternative data and explainable AI (XAI) - Develop real-time fraud detection with behavioral analytics, anomaly detection, and auto-decisioning - Create conversational agents for bill pay, account management, and member self-service - Automate invoice workflows: extraction, classification, approval routing, reconciliation - Partner with compliance/risk to keep AI decisions auditable, fair, and regulatory-compliant What you should have: - 5+ years software engineering; 3+ years in AI/ML or LLM engineering - 2+ years building AI for banking, credit unions, or financial services - Hands-on experience with Amazon Bedrock, LangChain, Python, AWS, and infrastructure-as-code - Working knowledge of NCUA, PCI-DSS, SOC 2, GLBA, and Fair Lending requirements - Bachelor's or Master's in Computer Science, Software Engineering, Data Science, or related field Nice to have: - AWS or Azure AI/ML certifications - Open-source LLM experience (Llama, Mistral, Phi) and self-hosted inference (vLLM, Ollama) - Vector databases (Pinecone, OpenSearch, pgvector) - Graph-based fraud networks and graph ML - AI governance / responsible AI framework experience - Prior work at a credit union, community bank, or fintech lending platform To apply, please share: - Your resume highlighting AI and banking project experience - A brief note on your most impactful AI agent or LLM project in a financial-services context - Links to GitHub, portfolio, or published papers (optional but encouraged)