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About This Role
As organizations adopt generative AI, securing how AI agents, models, and automated workflows access enterprise systems and data has become a core engineering challenge. As an Identity \& Gen AI Engineer, you will build generative AI solutions with identity, access, and trust engineered in from the start, securing both human and non\-human identities and governing how AI agents and GenAI platforms reach data and downstream systems. This role focuses on hands\-on engineering, integration, and continuous enhancement of AI solutions in which identity and access controls are a first\-class concern.
Work you'll do
As an Identity \& Gen AI Engineer on the Identity and Access Management team, you will be responsible for...
- Build and integrate generative AI solutions, including LLM applications, retrieval\-augmented generation, and AI agents, with secure access to data and downstream systems.
- Engineer authentication, authorization, and identity controls for AI agents, service accounts, and other non\-human identities operating across enterprise and cloud environments.
- Develop guardrails for agentic workflows, including scoped permissions, least\-privilege access, credential and secrets management, and runtime policy enforcement.
- Implement logging, monitoring, and governance that provide traceability and accountability for AI system actions.
- Collaborate with IAM, security architecture, and data teams to embed identity controls into GenAI solution delivery and operations.
- Create and maintain reference architectures, reusable patterns, and technical documentation for building and securing AI systems.
A successful candidate would possess these skills:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast\-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to provide clear guidance to others
The team
Our Deloitte Cyber team understands the unique challenges and opportunities businesses face in cybersecurity. Join our team to deliver powerful solutions to help our clients navigate the ever\-changing threat landscape. Through powerful solutions and managed services that simplify complexity, we enable our clients to operate with resilience, grow with confidence, and proactively manage to secure success.
Our Digital Trust \& Privacy offering enables trust and safety of online communications and digital products, protecting users, consumers, and patients from harm. Enables clients to provide consumer confidence in knowing with whom they are dealing and ensuring the integrity of access to data.
Qualifications
Required:
- Bachelor's degree in Cybersecurity, Computer Science, Information Systems, Engineering, or a similar technical field
- Ability to work onsite up to 5 days a week.
- 3\+ years of software engineering experience with Python or a comparable language
- 1\+ year of hands\-on experience building, integrating, or deploying generative AI solutions such as large language model (LLM) applications, retrieval\-augmented generation (RAG), or AI agents, including use of model APIs, orchestration frameworks, and AI development tools such as Claude Code, OpenAI Codex, GitHub Copilot, or Cursor
- Working knowledge of identity and access management concepts and protocols, including authentication, authorization, single sign\-on (SSO), and standards such as OpenID Connect (OIDC), Security Assertion Markup Language (SAML), OAuth, and JSON Web Token (JWT)
- Ability to travel 15%, on average, based on the work you do and the clients and industries/sectors you serve.
- Ability to obtain and maintain the necessary security clearance.
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Preferred:
- Experience deploying generative AI solutions to production environments
- Hands\-on experience with identity and access management platforms such as SailPoint, Okta, or Microsoft Entra ID
- Experience securing non\-human or machine identities, service accounts, secrets, and credentials using tools such as HashiCorp Vault or CyberArk
- Experience with AI agent frameworks and protocols such as LangChain, LangGraph, or Model Context Protocol (MCP)
- Experience with fine\-grained authorization or policy\-as\-code using tools such as Open Policy Agent (OPA), Cedar, or OpenFGA
- Familiarity with AI and LLM security risks such as the OWASP Top 10 for LLM Applications, prompt injection, and excessive agency
- Experience applying AI governance and risk frameworks such as the NIST AI Risk Management Framework (AI RMF)
- 2\+ years of experience building or deploying workloads in cloud environments such as Amazon Web Services (AWS) and Microsoft Azure
- Certified Information Systems Security Professional (CISSP), Certified Cloud Security Professional (CCSP), or a cloud engineering certification such as AWS Certified Solutions Architect or Microsoft Certified: Azure Solutions Architect
- 1\+ year of experience supporting federal government environments
1\+ year of experience with infrastructure\-as\-code or automation technologies such as Terraform or Ansible
*
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $105,400 to $207,800\.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
Salary Context
This $105K-$207K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($156K) sits 27% below the category median. Disclosed range: $105K to $207K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Deloitte AI Hiring
Deloitte has 59 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Engineer, Research Engineer. Positions span Rosslyn, VA, US, Baltimore, MD, US, Morristown, NJ, US. Compensation range: $140K - $379K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
Frequently Asked Questions
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