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About This Role
Three hundred fifty million Americans rely on a healthcare system whose decision\-making has become slow, costly, and adversarial \- care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI\-first effort, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that 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 an 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.
Work you'll do
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 across the industry, from clinical review and prior authorization to claims integrity and care management.
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 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.
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 \- engineered for one of the most complex operating environments in the world. The work spans the healthcare industry \- payers, providers, and life sciences \- and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs. We pair frontier AI research with production\-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.
Required 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, 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.
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, 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.
Compensation
Base salary is benchmarked to leading technology companies rather than traditional consulting scales, and the role carries a substantial performance\-based incentive opportunity designed to grow with the value you help create \- startup\-style upside, with the backing of a committed, well\-capitalized platform. The estimated base salary range is $134,500\-$265,100 (not adjusted for geographic differential); actual base pay depends on your skills, experience, and level, and you may also be eligible for a discretionary annual incentive based on individual and organizational performance.
Salary Context
This $134K-$265K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Deloitte, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($199K) sits 10% above the category median. Disclosed range: $134K to $265K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Deloitte AI Hiring
Deloitte has 77 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, Research Engineer. Positions span Stamford, CT, US, Austin, TX, US, Jersey City, NJ, US. Compensation range: $121K - $372K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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