Sr AI/ML Architect (Healthcare Data)

Job Details
Sr AI/ML Architect – (Healthcare Data)
Long Term Contract
100% Remote

The Senior AI Architect provides technical leadership for enterprise AI initiatives, designing scalable architectures across traditional ML, NLP, GenAI, RAG, and multi agent systems. The role requires deep healthcare domain understanding and experience building production grade AI systems at scale.

Key Responsibilities

Architecture & Design

·         Define end to end architecture for AI/ML and GenAI systems.

·         Design retrieval augmented generation (RAG) pipelines, vector database strategies, and LLM orchestration flows.

·         Architect multi agent workflows, tool calling agents, and reasoning chains.

·         Translate complex healthcare requirements into scalable, secure AI solutions.

·         Data & Pipeline Engineering

·         Architect event driven and incremental data pipelines for structured and unstructured healthcare data.

·         Oversee embedding generation, feature pipelines, data quality checks, and governance.

MLOps & LLMOps

·         Establish CI/CD pipelines for ML/LLM workloads.

·         Implement monitoring, evaluation, guardrails, lineage, and rollback strategies.

·         Ensure secure deployment across cloud platforms (AWS/Azure/Google Cloud Platform).

Leadership & Collaboration

·         Lead engineering teams; provide architectural direction and unblock developers.

·         Partner with product, business, and clinical SMEs to align solutions with objectives.

·         Communicate architecture decisions clearly to technical and non technical stakeholders.

·         Compliance & Responsible AI

·         Ensure adherence to privacy, security, and regulatory standards (HIPAA, PHI/PII).

·         Apply responsible AI principles, model guardrails, and safe deployment practices.

Required Qualifications

·         10–18+ years total experience, including 5+ years as an AI/ML Architect.

·         Strong expertise in LLMs, GenAI, RAG pipelines, embeddings, and vector databases.

·         Hands on experience with ML frameworks (PyTorch/TensorFlow) and orchestration tools (LangChain, LlamaIndex, Semantic Kernel).

·         Cloud architecture experience across AWS, Azure, or Google Cloud Platform.

·         Deep understanding of healthcare data (claims, EDI, clinical documents, payer/provider workflows)."