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.

Help create my AI clone and have it do YouTube videos.

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 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. NOTE: If you don't fully understand this job and you are just asking AI, don't apply. I will know if you are using AI to answer me on everything. I plan to do a thorough in person interview. 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 and 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 with 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.

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 a programmer with installation and setup experience in windsurf vibe coding, as well as the ability to train full stack programmers on how to use the code editor effectively as well as managing and monitoring the AI for correct inputs and execution.

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.

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.

Position Summary The Contract Writer is responsible for developing high-quality instructional content for CodeVA’s high school introductory computer science course titled Computer Science with Foundations with Python. This course covers 140 hours of instruction delivered over the course of a school year for high school credit (i.e., as an elective course). Working under the direction of the Program Development Lead and/or Create team project leads, this role contributes to the creation of curriculum, instructional materials, and program assets aligned to the learning goals, scope, and sequence of our Computer Science Foundations with Python course. Contract Writers produce clear, engaging, and instructionally sound written instructional content, ensuring lesson plan materials meet our high standard for quality, coherence, and usability. Contract writers will work closely with the project’s Product Development Lead to create lesson plans aligned to the course objectives, making decisions about how to achieve the instructional sequence and pace expressed in the course development plan (created by the Product Development Lead in advance of the project and edited with input as needed from the Contract Writer). Topics include: -Coding with Python (i.e., input, output, variables, conditionals, arrays/objects/dictionaries, loops, iteration, functional programming, object-oriented programming) -Computing networks and the Internet (i.e., network topology, protocols) -Cybersecurity (i.e., personal safety & digital hygiene, vulnerabilities, threats) -Basic data science with Python (i.e., creating simple visualizations, using Jupyter notebooks, pandas, matplotlib) -AI & machine learning (i.e., algorithmic bias, ML models, using simple “pre-baked” models with Python) Contract writers typically meet weekly with their supervisor to receive feedback and to claim new assignments as deliverables are completed. The goal of this project is to create ~40 lesson plans total to encompass about 140 instructional hours. Writers will claim lessons from the development list as they are able to complete them until all lessons are complete. There is no limit to the number of lessons a writer may create.