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.

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 are looking for a skilled, hands-on AI Engineer to help us build and optimize our AI product. You will be responsible for designing the AI architecture, integrating modern LLMs/frameworks, and ensuring our AI pipeline runs efficiently, reliably, and accurately in production. Responsibilities Design, build, and deploy custom AI solutions (LLM integration, RAG, AI agents, or fine-tuning). Build robust prompt engineering pipelines, function-calling workflows, or structured output mechanisms. Implement vector databases (e.g., Pinecone, Weaviate, Qdrant, ChromaDB) for semantic search and retrieval. Optimize latency, API costs, and context window efficiency across LLM providers (OpenAI, Anthropic, open-source models). Connect AI models to backend services via REST APIs / webhooks. Implement evaluation metrics (hallucination detection, retrieval accuracy, output validation). Required Skills & Qualifications Languages: Python (strong expertise required), TypeScript/Node.js (a plus). AI / ML Tooling: LangChain, LlamaIndex, AutoGen, CrewAI, or direct SDK integrations (OpenAI, Anthropic, Hugging Face). Databases: Vector databases (Pinecone, Chroma, Qdrant, pgvector) + relational/NoSQL DBs. Deployment & Cloud: Docker, AWS / GCP / Azure, FastAPI / Flask, Serverless architectures. Core Concepts: In-depth understanding of Embeddings, RAG, Fine-Tuning, Function Calling, and Agentic Workflows. Preferred (Nice to Have) Experience deploying open-source models locally or on dedicated hardware (vLLM, Ollama, Hugging Face TGI). Experience with fine-tuning techniques (LoRA, QLoRA). Background in frontend AI UI integration (Vercel AI SDK, Streamlit, Gradio).

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.

Hi I am looking to create an AI agent that is friendly with Copilot but could work with other common AI platforms. Details: An AI RFP agent to streamline the entire procurement process by drafting RFPs, recommending deliverables, checking for missing requirements, and ensuring USDA/AMS compliance. It could automatically compare vendor proposals, score them against predefined criteria, identify risks and gaps, recommend vendors based on past performance, and generate executive summaries and recommendation memos. Once a vendor is selected, it could also create statements of work, project timelines, and contract milestones while capturing lessons learned to continuously improve future RFPs. The result is a faster, more consistent, and more strategic procurement process that allows the team to spend less time on administration and more time selecting the best partners.

We are seeking an expert Data Scientist in the United States with 5+ years of experience in linear programming. The ideal candidate will have a strong background in data analysis and machine learning, with the ability to optimize complex systems using linear programming techniques. Responsibilities include developing and implementing data-driven solutions, collaborating with cross-functional teams, and providing insights to drive business decisions. TeamBuilder is a rapidly growing healthcare SaaS company on a mission to transform healthcare with our innovative technology. We believe in empowering our customers through inventive solutions and a commitment to excellence. Our young, rapidly growing team is looking for passionate professionals who thrive in a dynamic, innovative, and collaborative environment. ***This role is FULLY REMOTE, though you MUST reside within the United States and be legally authorized to work in the United States. The Role TeamBuilder builds software for ambulatory healthcare operations. The problem is simple: turn messy healthcare data into actions. This role sits between data, forecasting models, and optimization. You own forecasts end to end and work directly with the optimization layer. Shape inputs, validate outputs, and turn results into something operators can use. What You’ll Do  Forecasting model ownership o Build and iterate production grade demand and volume forecasts o Evaluate models with backtesting and real-world performance o Maintain stability with retraining, drift monitoring, and failure handling o Improve models based on operational outcomes  Sit at intersection of data and optimization o Define input data contracts for optimization models including capacity, constraints, and demand o Transform raw data into model-ready features and constraints o Validate solver outputs and identify infeasibility, constraint conflicts, and scaling issues o Trace issues back to data assumptions and constraint design o Convert outputs into usable schedules and recommendations  Model + system validation o Compare model output to operational reality and explain gaps o Run scenario analyses under different constraints o Improve pipelines that support forecasting and optimization quality o Test assumptions against real-world variation  Communication / translation o Explain models and outputs to non-technical users including operators and clients o Translate tradeoffs between accuracy, feasibility, and constraints o Deliver clear recommendations tied to business outcomes o Participate in client conversations and defend model behavior What We’re Looking For Qualifications  5+ years in data science or applied analytics  Experience owning forecasting models in production  Strong Python and SQL, comfortable with large datasets  Experience working with messy data  Ability to explain technical work clearly to non-technical users Preferred Skills  Experience in healthcare or complex operational environments such as staffing or capacity planning  Experience with capacity-constrained systems  Familiarity with solvers such as Gurobi Why You’ll Love Working Here  Mission-driven team tackling real healthcare challenges.  Freedom to experiment and innovate without layers of bureaucracy.  Opportunity to shape the company’s data science culture and R&D direction.  Flexible work environment and supportive, intellectually curious teammates. Additional Information  Job Type: Contracted, Part-Time/Full-Time, Remote  Compensation: Hourly, project-based. Potential to go Full-Time.  Culture: We foster a collaborative, engaging, mission-driven culture that values innovation and prioritizes customer success.

We are developing a digital-twin workflow for an oil & gas production facility using drone imagery, ground photography, video, LiDAR/RTK where available, and AI-assisted component identification. We need a freelancer with experience in computer vision, 3D reconstruction/digital twins, Python, Power BI, photogrammetry, LiDAR, or oil & gas process equipment to evaluate the existing methodology and recommend/build the most practical workflow to: reconstruct or organize the facility as a digital twin; identify and count fugitive-emission components such as valves, flanges/connectors, open-ended lines, instruments, gauges, relief valves, pump/compressor seals, sight glasses, etc.; assign each component a unique ID and avoid duplicate counting across multiple images; associate components with process service such as Gas/Vapor, Light Oil, or Water/Light Oil; create an auditable component database with image/location references and confidence level; generate summary tables, component counts, QA/QC views, and reports using Power BI, Python, or another suitable platform; recommend where existing AI models, object detection/segmentation, photogrammetry, or manual validation should be used. Deliverable: a concise technical recommendation plus a working prototype using a sample facility dataset, showing the proposed digital-twin/component-count workflow and final reporting format. Oil & gas production-facility experience is strongly preferred. Knowledge of YOLO/object detection, OpenCV, COLMAP/photogrammetry, point clouds/LiDAR, Python, Power BI, or GIS is a plus. The objective is not simply object detection; it is to create a repeatable, auditable methodology for regulatory fugitive-component inventory and counting.

Seeking an experienced PyTorch engineer with strong knowledge of transformer internals to help implement a model-specific runtime intervention module within an existing supervisory architecture. The core architecture, evaluator, control logic, interfaces, and project structure are already implemented. This engagement focuses on implementing and validating the actuator layer that translates neutral control directives into model-specific residual-stream operations.

-$150K Salary with Healthcare benefits, W2 applicants ONLY. -Must be U.S. Citizen -Remote role in U.S. with 25% travel to client sites Show me your best work as a Forward Deployed AI Engineer (FDE) by showing me: a) how you approach enterprise clients' complexities ($1B+ revenue businesses)** experience is a must with enterprise b) your technical fluency across data, tech, AI c) your people skills d) how you break down business processes e) experience around customer experience work preferred** Claude Certified Architects, Codex, and all other LLMs/AI engineering tools Only interviewing serious candidates looking for full-time work. No agencies, or LLCs/s-corps.... only FTEs W2. ex-Big 4, Big Tech Preferred candidates will get interviews first. Applicants that use AI slop to apply won't be considered.

FrantzMaster AI is an all-in-one intelligent personal, business, transportation, finance, savings, reminder, and daily-life assistant designed to help people save money, make better decisions, stay organized, and manage everything in one place. The main purpose of FrantzMaster AI is to become a user’s everyday AI assistant for finding the best opportunities, comparing prices, tracking important expenses, managing bills, discovering affordable transportation and fuel options, monitoring business activities, and reminding users about important tasks before they forget them. FrantzMaster AI should be simple enough for anyone to use while being powerful enough to help individuals, families, drivers, truck drivers, small-business owners, entrepreneurs, and companies. The application should use artificial intelligence, automation, location-based services, personalized recommendations, price comparisons, calculators, alerts, reminders, dashboards, and intelligent tracking to help users save time and money. 1. SMART AI PERSONAL ASSISTANT FrantzMaster AI should have a central AI assistant that users can communicate with naturally. Users should be able to type or speak requests such as: * “Find me the cheapest gas near me.” * “Find the best load for my truck.” * “Find me a cheap ride.” * “Remind me to pay my insurance tomorrow.” * “How much money did I spend this month?” * “How much will this trip cost me?” * “Find the best loan for me.” * “Compare these insurance prices.” * “Remind me about my car payment.” * “What bills do I have coming up?” * “How much can I save this month?” * “Find the cheapest route.” * “Calculate my profit.” * “Help me manage my business.” * “What should I pay first?” * “Show me everything I need to do today.” The AI should understand the user’s request and automatically direct them to the correct feature. ⸻ 2. BEST LOAD FINDER FrantzMaster AI should include a powerful Load Finder designed especially for truck drivers, box-truck drivers, owner-operators, carriers, and transportation businesses. The system should help users find available loads and compare them. Users should be able to enter: * Truck type * Truck size * Maximum weight * Current location * Destination * Available dates * Preferred distance * Fuel economy * Minimum desired payout * Return-trip preferences * Empty-mile preferences The AI should help compare loads based on: * Total payout * Miles * Estimated fuel cost * Tolls * Estimated driving time * Deadhead miles * Estimated profit * Profit per mile * Profit per hour * Pickup location * Delivery location * Broker information * Load requirements The AI should help users understand which loads may provide better potential profit. The app should never guarantee that a load is profitable. Instead, it should provide calculations and estimates so the user can make an informed decision. ⸻ 3. CHEAPEST GAS FINDER FrantzMaster AI should include a Best Gas Finder. The user should be able to see nearby gas stations and compare fuel prices. The application should allow users to search for: * Cheapest regular gas * Cheapest mid-grade * Cheapest premium * Cheapest diesel * Gas stations near the user * Gas stations along a route * Gas stations near a destination The AI should consider: * Distance * Fuel price * Estimated amount of fuel needed * Vehicle fuel economy * Potential savings * Route convenience The system should help users avoid driving far out of their way just to save a few cents per gallon. ⸻ 4. LOAN COMPARISON FrantzMaster AI should include a Loan Finder and Loan Comparison Tool. Users should be able to enter: * Loan amount * Credit score range * Desired loan term * Monthly income * Monthly expenses * Down payment * Interest rate * Existing debt The AI should help users compare loan offers based on: * APR * Interest rate * Monthly payment * Total interest * Total repayment * Loan term * Fees The AI should clearly explain that loan offers, approval decisions, interest rates, and eligibility depend on the lender and the user’s financial information. The app should help users understand loans rather than promise approval. ⸻ 5. INSURANCE COMPARISON FrantzMaster AI should help users organize and compare insurance information. The app can support categories such as: * Car insurance * Truck insurance * Commercial insurance * Home insurance * Renters insurance * Business insurance * Life insurance * Other insurance Users should be able to save: * Insurance company * Policy number * Monthly payment * Due date * Renewal date * Coverage information * Agent information * Customer service information The AI should remind users before payments and renewals. ⸻ 6. CAR AND TRUCK MANAGEMENT FrantzMaster AI should have a Vehicle Manager. Users should be able to add: * Car * SUV * Van * Pickup truck * Box truck * Commercial truck * Trailer * Motorcycle For every vehicle, users should be able to track: * VIN * License plate * Mileage * Registration expiration * Insurance expiration * Inspection * Oil changes * Tire rotations * Brake service * Maintenance * Repairs * Fuel expenses * Loan payments * Vehicle value * Service history The app should automatically remind users when important vehicle tasks are approaching. ⸻ 7. BILL MANAGEMENT FrantzMaster AI should include a powerful Bill Manager. Users should be able to add every recurring bill they have.