AI/ML Engineer ($50/hr)

Key Responsibilities: Design and deploy LLM-powered and agentic AI applications, including tool/function calling, multi-agent orchestration, and MCP integrations. Build and optimize RAG pipelines using embeddings, vector databases, retrieval, re-ranking, and grounding techniques. Develop document intelligence solutions for extraction, classification, OCR, and structured data generation. Build evaluation, human-in-the-loop, and feedback processes to improve AI accuracy and reliability. Implement observability for AI/ML systems, monitoring quality, latency, cost, token usage, and model performance. Develop production-grade Python services and deploy them using Docker, CI/CD, Kubernetes, and cloud-native technologies. Partner with business, clinical, and operational teams to translate complex problems into scalable AI solutions. Apply Responsible AI, governance, security, evaluation, and model risk management practices.

Required Qualifications: 5+ years of experience building and deploying ML/AI systems, including recent hands-on experience with LLMs or agentic AI.Strong foundation in machine learning, deep learning, NLP, and transformer architectures. Expert-level Python and experience with PyTorch and/or TensorFlow. Hands-on experience with LLM platforms such as Azure OpenAI, OpenAI, or Anthropic. Experience with RAG, vector databases, embeddings, AI agents, and frameworks such as LangChain, LangGraph, or LlamaIndex. Strong SQL and experience building data/ML pipelines using tools such as Airflow, Spark, Databricks, or Snowflake. Experience with Docker, Git/GitHub, CI/CD, and cloud platforms, preferably Azure. Knowledge of ML/LLM evaluation and observability tools such as Arize, Langfuse, or similar. Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent experience.

Preferred: Experience in healthcare, insurance, or another regulated/high-stakes environment. Knowledge of Responsible AI, model governance, validation, and AI security. Experience with Kubernetes, Terraform, Kafka, or infrastructure as code. Experience working with clinical or operational subject matter experts and designing AI solutions that support expert review and explainability.