AI/ML Engineer

AI ML Engineer

Technical Expertise

• Strong expertise in Machine Learning, Deep Learning, NLP, Large Language Models (LLMs), and Generative AI.

• Hands-on experience with RAG, Vector Databases, Knowledge Graphs, AI Agents, and Agentic AI frameworks.

• Proficiency in Python, Java, APIs, Microservices, and distributed system architecture.

• Strong knowledge of Azure AI Services, Azure OpenAI, AWS SageMaker, or Google Vertex AI.

• Experience with Databricks, Snowflake, Spark, Kafka, and modern data engineering platforms.

• Expertise in Kubernetes, Docker, GitHub Actions/Azure DevOps, Terraform, and cloud-native architectures.

• Knowledge of Responsible AI, AI Governance, Model Risk Management, and AI Security principles.

Key Responsibilities

• Design, build, and deploy LLM powered and agentic AI applications, including multi agent orchestration, tool/function calling, and MCP based integrations.

• Develop and optimize RAG pipelines — chunking, embedding, retrieval, re ranking, and grounding — with a focus on provenance and factual accuracy.

• Build document intelligence workflows (extraction, classification, OCR, structuring) over complex clinical and operational documents.

• Implement human in the loop review gates and feedback loops that let subject matter experts correct, validate, and improve model output.

• Instrument systems for ML observability — latency, cost, token usage, drift, and quality — and act on what the telemetry shows.

• Write production grade Python, containerize services, and operate them through CI/CD and orchestration tooling.

• Collaborate with product, clinical, and operations partners to translate ambiguous business problems into reliable AI systems.

• Uphold Responsible AI practices: evaluation, bias/error analysis, guardrails, and clear documentation.

Required Skills

•Machine Learning & AI foundations

• Strong grounding in ML fundamentals — supervised/unsupervised learning, evaluation methodology, and model selection.

• Practical experience with deep learning frameworks (PyTorch and/or TensorFlow).

• Solid understanding of NLP and transformer architectures.

Generative AI, LLMs & Agentic Systems

• Hands on experience building with LLMs (OpenAI/Azure OpenAI, Anthropic Claude, or comparable).

• Prompt engineering, structured outputs, and function/tool calling.

• Experience with agentic frameworks and orchestration (e.g., LangChain, LangGraph, LlamaIndex, or equivalent) and multi agent design patterns.

• RAG system design: vector databases, embeddings, retrieval and re ranking strategies, and grounding/citation techniques.

• Familiarity with the Model Context Protocol (MCP) or similar tool/integration standards.

Data Engineering

• Strong SQL and experience with relational databases (SQL Server, PostgreSQL, or similar).

• Building and maintaining data/ML pipelines and workflow orchestration (Airflow or equivalent).

• Comfort working with unstructured and semi structured data at scale.

MLOps & Observability

• Model/LLM evaluation frameworks and offline/online testing.

• Observability tooling for AI systems (e.g., Arize, Langfuse, or comparable) — monitoring quality, cost, drift, and token usage.

• Experiment tracking and reproducibility practices.

Software Engineering & Cloud

• Expert level Python and sound software engineering habits (testing, code review, version control with Git/GitHub).

• Containerization with Docker and CI/CD (GitHub Actions or equivalent).

• Cloud platform experience (Azure preferred; AWS/GCP acceptable), including deploying and scaling services.

Preferred / Nice to Have

• Experience with AI in healthcare, insurance, or another regulated, high-stakes domain.

• Familiarity with Responsible AI frameworks, model governance, and validation gates.

• Experience designing UIs or interaction patterns for AI assisted expert review (trust, explainability, click to evidence).

• Kubernetes and infrastructure as code exposure.

• Experience working with clinical or operational subject matter experts.

Qualifications

• Bachelor's or Master's in Computer Science, Data Science, Machine Learning, or a related field (or equivalent practical experience).

• [5]+ years building and shipping ML/AI systems, including recent hands-on work with LLMs or agentic applications.

What Makes You a Fit

• You care about correctness and are comfortable engineering for a world where AI errors will happen — building the guardrails, evaluation, and human oversight to catch them.

• You can communicate technical trade offs to non-technical clinical and business partners.

• You take ownership, work deliberately, and iterate based on real usage and data.