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.