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.

CONTACTING OUTSIDE OF UPWORK WILL RESULT IN AUTOMATIC DISQUALIFICATION Description: We're an agency staffing infrastructure and MLOps talent across a portfolio of active platform builds for confidential enterprise clients. Work includes provisioning environments inside client-owned cloud infrastructure under their security review process, and building CI/CD and observability for both application and production ML systems. Immediate need — looking to onboard within the next 5-7 business days. This role is typically front-loaded (environment setup at kickoff) then lighter mid-build, ramping again ahead of go-live — hours will flex accordingly rather than staying flat throughout. What you'll do: Stand up and manage CI/CD pipelines for both application and ML model deployment Implement model registry and shadow/champion-challenger release patterns with automated rollback Build observability — logging, monitoring, alerting — across application and ML layers Provision and manage environments within client-owned cloud infrastructure per their security review requirements Support drift monitoring and retraining triggers for production ML systems Required: Strong DevOps/infrastructure-as-code background (Terraform, CloudFormation, or similar) CI/CD pipeline design experience, including for ML model deployment specifically (MLOps) Cloud platform experience (AWS, GCP, or Azure), including provisioning within a client-owned/managed environment Available to start within the next week Nice-to-have: ML monitoring/observability tooling (drift detection, model registries such as MLflow), security-review or compliance-adjacent infrastructure experience

CONTACTING OUTSIDE OF UPWORK WILL RESULT IN AUTOMATIC DISQUALIFICATION Description: We're an agency staffing infrastructure and MLOps talent across a portfolio of active platform builds for confidential enterprise clients. Work includes provisioning environments inside client-owned cloud infrastructure under their security review process, and building CI/CD and observability for both application and production ML systems. Immediate need — looking to onboard within the next 5-7 business days. This role is typically front-loaded (environment setup at kickoff) then lighter mid-build, ramping again ahead of go-live — hours will flex accordingly rather than staying flat throughout. What you'll do: Stand up and manage CI/CD pipelines for both application and ML model deployment Implement model registry and shadow/champion-challenger release patterns with automated rollback Build observability — logging, monitoring, alerting — across application and ML layers Provision and manage environments within client-owned cloud infrastructure per their security review requirements Support drift monitoring and retraining triggers for production ML systems Required: Strong DevOps/infrastructure-as-code background (Terraform, CloudFormation, or similar) CI/CD pipeline design experience, including for ML model deployment specifically (MLOps) Cloud platform experience (AWS, GCP, or Azure), including provisioning within a client-owned/managed environment Available to start within the next week Nice-to-have: ML monitoring/observability tooling (drift detection, model registries such as MLflow), security-review or compliance-adjacent infrastructure experience

Lead the development of an AI-agent platform that autonomously analyzes financial transactions, customer activity, cash flow, and risk signals to support FinTech operations. The system will use LLM-based agents, ML models, RAG, and real-time financial data to investigate anomalies, assess risk, generate financial insights, and recommend actions. Key Responsibilities: Define the AI-agent architecture, product roadmap, agent workflows, and evaluation strategy. Design specialized agents for fraud investigation, transaction analysis, risk assessment, cash-flow analysis, and financial reporting. Combine deterministic financial rules with ML predictions and LLM reasoning rather than relying solely on LLM outputs. Build agent orchestration using LangGraph/LangChain, tool calling, structured outputs, memory, and RAG. Develop ML pipelines for anomaly detection, behavioral scoring, transaction classification, and risk prediction. Implement human-in-the-loop approvals, confidence scoring, audit trails, and agent observability. Establish evaluation frameworks for agent accuracy, hallucination detection, tool-use reliability, and financial decision quality. Work with engineering teams to productionize agents using Python, FastAPI, PostgreSQL, AWS, Docker, and MLflow. Core Technologies: Python, PyTorch, Scikit-learn, XGBoost/LightGBM, LangGraph, LangGraph, LLMs, RAG, vector databases, PostgreSQL, FastAPI, AWS, Docker, MLflow, REST APIs, Databricks, and event-driven architectures. Core ML Libraries: - Deep Learning: TensorFlow, PyTorch, Lightning - Classical ML: Scikit-learn, XGBoost, LightGBM - NLP/LLMs: Hugging Face Transformers, spaCy - Hyperparameter Tuning: Optuna, Ray Tune Infrastructure & Tools: - Cloud: AWS/GCP/Azure (S3, BigQuery, Sagemaker) - MLOps: MLflow, Kubeflow, Prefect - Data: SQL, Pandas, PySpark, Dask - Deployment: Docker, Kubernetes, FastAPI Expected Outcome: A production-ready multi-agent FinTech intelligence system where AI agents investigate financial events, combine ML predictions with financial rules, retrieve supporting data, explain their reasoning, and route high-risk decisions to human reviewers. Skills Artificial Intelligence (AI) Machine Learning AI Agent Development RAG PyTorch Scikit-learn Databricks MLOps

We are looking for an experienced AI Architect / Senior LLM Engineer to design and build an enterprise-grade AI platform for the healthcare industry. You will lead the architecture and implementation of intelligent AI solutions that improve clinical operations, automate administrative workflows, and enable healthcare professionals to access trusted medical knowledge through advanced AI technologies. The ideal candidate has hands-on experience building production-ready Agentic AI systems, Multi-Agent architectures, RAG pipelines, and LLMOps using modern AI frameworks and cloud platforms. Responsibilities Design and develop scalable Agentic AI solutions for healthcare applications. Build Multi-Agent Systems using LangGraph, CrewAI, or AutoGen. Develop enterprise Retrieval-Augmented Generation (RAG) pipelines for medical knowledge retrieval. Create AI agents for clinical knowledge assistance, document intelligence, workflow automation, and care coordination. Build and integrate MCP servers and custom AI tools with internal healthcare systems. Optimize prompt engineering, retrieval strategies, and response quality for high accuracy. Implement AI guardrails, evaluation pipelines, monitoring, and observability for production deployments. Deploy secure, scalable AI infrastructure on AWS using Infrastructure as Code and CI/CD best practices. Collaborate with engineering, product, and healthcare stakeholders to deliver reliable AI solutions. Required Skills 5+ years of experience in AI/ML or Generative AI development. Strong expertise in Python and backend API development. Experience with LangGraph, CrewAI, AutoGen, or similar multi-agent frameworks. Hands-on experience with AWS Bedrock, Azure OpenAI, or Vertex AI. Strong understanding of RAG architectures, vector databases, embeddings, and semantic search. Experience with Pinecone, Weaviate, pgvector, or similar vector databases. Knowledge of LLMOps, evaluation frameworks, prompt engineering, and AI observability tools. Experience with Docker, Terraform, CI/CD, and cloud-native deployments. Familiarity with healthcare compliance, security, and responsible AI practices is highly preferred. Preferred Technologies LangGraph CrewAI AutoGen AWS Bedrock Claude GPT-4o Gemini Pinecone pgvector LangSmith Arize Phoenix FastAPI Docker Terraform GitHub Actions MLflow Nice to Have Experience developing AI-powered healthcare platforms. Knowledge of healthcare workflows, clinical documentation, or medical knowledge systems. Experience integrating AI solutions with enterprise applications through APIs and MCP. Familiarity with AI governance, model evaluation, and production monitoring. If you are passionate about building enterprise-scale AI solutions that transform healthcare through Agentic AI and Generative AI, we'd love to hear from you.