Agentic AI Engineer — Healthcare AI

Deloitte seeks an Agentic AI Engineer to design, build, and deploy LLM- and SLM-powered agentic systems for healthcare decisioning, delivering end-to-end architectures from development to production in live clinical and operational settings. This onsite role is based in Jacksonville, Florida.

Responsibilities

• Develop agentic systems capable of multi-step reasoning, planning, tool integration, and workflow execution within complex, regulated operating processes.
• Construct stateful workflows using LangGraph and LangChain or equivalents, including branching, retries, self-correction, human-in-the-loop checkpoints, and reusable orchestration patterns.
• Engineer for long-horizon reliability, ensuring multi-step task completion, recovery from cascading errors, planning under uncertainty, and robust tool use when individual steps fail.
• Shape the reasoning behind regulated decisions with policy- and criteria-grounded outputs, structured proposer/critic/judge style reviews, and auditable rationales for high-stakes decisions across clinical review, prior authorization, claims integrity, and care management.
• Develop end-to-end Retrieval-Augmented Generation pipelines, covering ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.
• Engineer memory and context management, including conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context selection.
• Apply contemporary context-delivery patterns to ensure 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.
• Incorporate guardrails, safety controls, and robust failure-handling to reduce hallucinations and unsafe actions.
• Evaluate agents at trajectory and task levels, including multi-step task success, failure modes, regression analysis, sandboxed testing, and integration with retrieval- and generation-quality metrics, automated checks, and human review.
• Implement healthcare-grade safety through 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 to ensure safe operation within real business workflows.
• Deliver production-quality code with strong testing, CI/CD, logging, version control, and documentation; make architecture decisions balancing quality, safety, latency, cost, and model risk.
• Collaborate with modeling and post-training engineers to improve model behavior for tool use, grounding, and long-horizon reasoning via evaluation-driven feedback and, when helpful, 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.

Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related field.
• Proven track record of building and shipping production agentic systems, with emphasis on hands-on, recent work rather than exploratory projects.
• Solid experience developing production agent systems with modern orchestration, LangGraph/LangChain or equivalents, including custom orchestration.
• Experience designing and optimizing end-to-end RAG systems, including 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 knowledge of LLM behavior, including strengths, limitations, hallucination risks, reasoning constraints, and latency/cost trade-offs, plus methods to evaluate them.
• Experience evaluating and debugging agent behavior using task-success and trajectory analysis, not solely output quality.
• Proficiency in Python and modern software practices: testing, CI/CD, version control, 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) or open-weight/self-hosted models (e.g., Llama via vLLM), including production tool use and agent capabilities.
• Ability to travel up to 50 percent, depending on client needs and project scope.
• Limited immigration sponsorship may be available.

Technologies

• LangGraph
• LangChain
• Retrieval-Augmented Generation (RAG)
• Python
• vLLM
• Llama
• Anthropic
• Google
• OpenAI
• Pinecone
• Weaviate
• Milvus

The Team

Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure. The work spans the healthcare ecosystem, including payers, providers, and life sciences, and involves complex reasoning, nuanced operational workflows, and demanding performance standards.

Preferred Qualifications

• 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 like LoRA or QLoRA.
• Solid grounding in 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 or standards such as FHIR is a plus, not a requirement.
• Proven habit of staying current with AI research, benchmarks, and emerging engineering patterns.

Compensation

The base salary aligns with leading technology firms and is complemented by a substantial performance-based incentive program. The estimated base salary range is $110,700 to $372,900 (not adjusted for geographic differential); actual pay depends on skills, experience, and level.