Senior Agentic AI Engineer — Healthcare AI

Deloitte•Jersey City, NJ

85d•$110,700 - $372,900•Hybrid

About The Position

Deloitte has a new AI-first effort, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how the healthcare system decides — across payers, providers, and life sciences, and for the patients they serve — so that care is faster, fairer, and far less wasteful. This is not AI applied at the margins. It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale. This is an early, well-funded build. You will own agent systems end to end — from architecture through production — and your work ships into live clinical and operational settings within your first months, not into a lab. As a Senior Agentic AI Engineer, you will design, build, and operationalize the LLM- and SLM-powered systems behind real healthcare decisioning — the reasoning, orchestration, retrieval, memory, and control layers that let intelligent agents operate reliably across the hardest decisions in the industry: clinical reasoning, prior authorization and claims integrity, care navigation, and the operational workflows that run across payers, providers, and life sciences. This is not a prompt-only role. We are looking for builders who think deeply about system behavior, grounding, and reliability where a wrong action has real consequences for patients and the clinicians who serve them. You do not need a healthcare background. We pair every engineer with clinical and domain experts and teach you the domain — you bring the agentic engineering depth. We hire on demonstrated depth, not years — the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.

Requirements

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, not a recent exploration. We weigh shipped systems, research, model releases, and open source over years in a title; expect strong software/ML fundamentals plus substantial, recent hands-on agentic work.
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.
Strong 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.
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, based on the work you do and the clients and industries/sectors you serve.
Limited immigration sponsorship may be available.

Nice To Haves

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 — clinical, payer, or life-sciences workflows, or standards such as FHIR — is a plus, not a requirement.
Demonstrated habit of staying current with AI research, benchmarks, and emerging engineering patterns.

Responsibilities

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 across the industry, from clinical review and prior authorization to claims integrity and care management.
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.
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.
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 our modeling and post-training engineers to improve model behavior for tool use, grounding, and long-horizon reasoning — through evaluation-driven feedback and, where it helps, fine-tuned or reasoning-optimized models.
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.