Deloitte offers a compelling path for designing, building, and operationalizing agentic AI systems for healthcare decisioning as part of an AI-first initiative. This onsite role in Gilbert, AZ places you in live clinical and operational settings within your first months, collaborating with a cross-disciplinary team to shape outcomes across payers, providers, and life sciences. The position comes with a competitive base salary and a substantial performance-based incentive, backed by a well-capitalized platform and opportunities to grow with the value you help create.
You will join a team that blends 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 in one of the world’s most complex operating environments. The role centers on healthcare workflows that require careful reasoning, robust safety, and regulatory awareness.
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 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 errors, planning under uncertainty, and robust tool use when steps fail.
- Develop the reasoning behind regulated decisions with policy- and criteria-grounded outputs, structured review styles, and auditable rationales across clinical review, prior authorization, claims integrity, and care management.
- Develop end-to-end Retrieval-Augmented Generation pipelines: ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.
- Engineer memory and context management, including conversational state, persistent memory, retrieval-driven context assembly, and token-efficient context selection.
- Apply modern 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.
- Apply guardrails, safety controls, and failure-handling to reduce hallucinations and unsafe actions.
- Evaluate agents at trajectory and task levels, including multi-step task success, failure-mode analysis, sandboxed testing, and integration with retrieval- and generation-quality metrics.
- Engineer healthcare-grade safety with deployment eval gates, human oversight models, and PHI/HIPAA-aware data handling for regulated decisions.
- 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 strong 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, using evaluation-driven feedback.
- Translate ambiguous, high-complexity operational processes into robust system logic and reusable AI patterns, staying current with agentic systems advances.
Requirements
- Bachelor's degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related field.
- Demonstrated depth shipping production agentic systems as a primary craft, with strong software and ML fundamentals and substantial, recent hands-on agentic work.
- Hands-on experience building production agent systems with modern orchestration such as LangGraph/LangChain or equivalent, including custom orchestration.
- Experience designing and optimizing end-to-end RAG systems: indexing, retrieval, reranking, grounding, and evaluation.
- Strong memory and context management capabilities, including context windows, retrieval-driven context assembly, persistent memory, and high-signal context selection.
- Deep, practical understanding of LLM behavior, including strengths, limitations, hallucination risks, reasoning constraints, and latency/cost trade-offs, plus evaluation methods.
- Experience evaluating and debugging agent behavior through task-success and trajectory analysis.
- Strong Python engineering skills and modern software practices: testing, CI/CD, version control, API integration; experience implementing observability, tracing, and debugging for production LLM-based systems.
- 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) in production tool use and agent capabilities.
- Ability to travel up to 50 percent depending on work and client needs; limited immigration sponsorship may be available.
Technologies
- LangGraph, LangChain, Python, vLLM, Llama via vLLM, 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 industry, including payers, providers, and life sciences, and involves genuinely hard reasoning problems, nuanced workflows, and a high bar for quality and safety.
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.
- Understanding of traditional NLP concepts: tokenization, semantic similarity, entity extraction, summarization, and transformer fundamentals.
- Experience operating in regulated or high-stakes environments; healthcare exposure or standards such as FHIR is a plus, not required.
- Proven habit of staying current with AI research, benchmarks, and engineering patterns.
Compensation
Base salary ranges from $110,700 to $372,900 per year, with geographic adjustments not applied here. The role includes a substantial performance-based incentive opportunity, offering startup-style upside backed by a committed, well-capitalized platform.
