Agentic AI Engineer — Healthcare AI

Join Deloitte as an Agentic AI Engineer focused on healthcare, where you will design, build, and operationalize end-to-end agentic AI systems that support clinical and operational decisioning as part of Deloitte’s AI-first initiative. The role is grounded in real-world healthcare workflows and will see you shipping into live environments within the first months, onsite in Gilbert, AZ.

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 with multi-step task completion, recovery from compounding errors, planning under uncertainty, and robust tool use when individual steps fail.
• Develop the reasoning behind regulated decisions with policy- and criteria-grounded outputs, structured proposer/critic/judge-style review, and auditable rationales for high-stakes outcomes across clinical review, prior authorization, claims integrity, and care management.
• Build end-to-end Retrieval-Augmented Generation (RAG) 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 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 with multi-step task success, failure-mode analysis, sandboxed testing, and metrics for retrieval, generation, automation checks, and human review.
• Engineer 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 operate safely within real business workflows.
• Deliver production-quality code with robust testing, CI/CD, logging, versioning, and documentation; balance quality, safety, latency, cost, and model risk in architecture decisions.
• Collaborate with modeling and post-training engineers to improve model behavior for tool use, grounding, and long-horizon reasoning through evaluation-driven feedback and, where 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.
• Demonstrated depth building and shipping production agentic systems; this is your primary craft, with a track record of shipped systems, research, model releases, or open source work recent and substantial.
• Strong hands-on experience building production agent systems with modern orchestration using 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, including strengths and limitations, hallucination risks, reasoning constraints, and latency/cost trade-offs, plus evaluation methods.
• Experience evaluating and debugging agent behavior with task-success and trajectory analysis, beyond output quality alone.
• Strong Python engineering skills and modern software practices including 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) or open-weight/self-hosted models (e.g., Llama via vLLM), including production tool use and agent capabilities.
• Ability to travel 0-50 percent, depending on work and client engagements.
• Limited immigration sponsorship may be available.

Technologies

• LangGraph, LangChain, Python, vLLM, Llama via vLLM, Pinecone, Weaviate, Milvus

The Team

Deloitte combines AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate vertical AI systems across software, data, models, and cloud infrastructure. The healthcare focus spans payers, providers, and life sciences, addressing complex reasoning problems and intricate operational workflows in regulated environments.

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 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 environments; healthcare exposure or standards like FHIR is a plus.
• Demonstrated habit of staying current with AI research and emerging engineering patterns.

Compensation

The base salary is benchmarked to leading technology firms, with a substantial performance-based incentive and startup-style upside backed by a committed, well-capitalized platform. The estimated base salary range is $110,700-$372,900 per year (not adjusted for geographic differential); actual base pay depends on skills and experience.