Agentic AI Engineer, Healthcare AI

About the Role

Deloitte is building reasoning models and agentic systems to transform healthcare decision-making for payers, providers, life sciences, and patients. This is an early, well-funded initiative focused on a ground-up rebuild of the decision-making machinery behind American healthcare. As an Agentic AI Engineer, you will own agent systems end-to-end, from architecture through production, with work shipping into live clinical and operational settings.

Key Responsibilities

• Agent Architecture & Orchestration:

• Design and implement agentic systems capable of multi-step reasoning, planning, tool use, and workflow execution against complex, regulated operational processes.

• Build stateful workflows using frameworks such as LangGraph and LangChain—including branching, retries, self-correction, human-in-the-loop checkpoints, and reusable orchestration patterns.

• Engineer for long-horizon reliability—multi-step task completion, recovery from compounding errors, planning under uncertainty, and robust tool use when individual steps fail.

• Build the reasoning behind regulated decisions—policy- and criteria-grounded outputs, structured proposer/critic/judge-style review, and auditable rationales for high-stakes decisions.

• Retrieval, Grounding & Context Engineering:

• Develop end-to-end Retrieval-Augmented Generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.

• Engineer memory and context management—conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context selection.

• Apply modern context-delivery patterns (e.g., MCP-style tool/context interfaces) so agents access the right information at the right time.

• Reliability, Evaluation & Safety:

• Implement observability and tracing for prompts, tool calls, retrieval quality, agent traces, failures, drift, latency, and production behavior.

• Apply guardrails, safety controls, and failure-handling to reduce hallucinations and unsafe actions.

• Evaluate agents at the trajectory and task level—multi-step task success, failure-mode and regression analysis, and sandboxed test environments—alongside retrieval- and generation-quality metrics, automated checks, and human review.

• Engineer healthcare-grade safety—deployment eval gates, human-oversight and escalation models, auditability and traceability for regulated decisions, and PHI/HIPAA-aware data handling.

• Integration & Production Craft:

• Build integrations with internal and external tools, APIs, enterprise systems, databases, and model providers so agents operate safely within real business workflows.

• Deliver production-quality code with strong practices in testing, CI/CD, logging, versioning, and documentation; make architecture decisions that balance quality, safety, latency, cost, and model risk.

• Partner with modeling and post-training engineers to improve model behavior for tool use, grounding, and long-horizon reasoning.

• Translate ambiguous, high-complexity operational processes into robust system logic and reusable AI patterns; stay current with advances in agentic systems and translate research into practical engineering decisions.

Requirements & Tech Stack

• Python engineering skills and modern software practices: testing, CI/CD, version control, and API integration.
• Experience implementing observability, tracing, and debugging for LLM-based systems in production.
• Strong, hands-on experience building production agent systems with modern orchestration: LangGraph/LangChain or equivalent, including custom orchestration.
• Experience designing and optimizing end-to-end RAG systems: indexing, retrieval, reranking, grounding, and evaluation.
• Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal context selection.
• Deep, practical understanding of LLM behavior—strengths, limitations, hallucination risks, reasoning constraints, and latency/cost trade-offs—and the evaluation methods used to measure them.
• Experience evaluating and debugging agent behavior—task-success and trajectory analysis, not just output quality.
• Hands-on experience with at least one frontier model platform (e.g., Anthropic, Google, OpenAI) and/or open-weight/self-hosted models (e.g., Llama via vLLM), including production tool use and agent capabilities.
• Ability to travel 0-50%, on average.
• Limited immigration sponsorship may be available.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related field.
• Demonstrated depth building and shipping production agentic systems—this is your primary craft.
• Experience with multi-agent systems and agent collaboration patterns.
• Familiarity with vector databases and retrieval infrastructure such as Pinecone, Weaviate, or Milvus.
• Exposure to model adaptation and fine-tuning techniques such as LoRA or QLoRA.
• Understanding of traditional NLP concepts: tokenization, semantic similarity, entity extraction, summarization, and transformer fundamentals.
• Experience operating in highly regulated, high-stakes, or operationally complex environments. Healthcare exposure is a plus, not a requirement.
• Demonstrated habit of staying current with AI research, benchmarks, and emerging engineering patterns.