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About This Role
At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise\-scale impact, pairing leading class engineering with pod\-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem\-solving, and client impact, this role puts you at the forefront of AI transformations.
Work you'll do
As a Lead Frontier GenAI FDE, you will serve as the senior practitioner\-leader embedded directly with our most strategic clients, leading forward\-deployed engineering pods that develop and deploy GenAI solutions into production for Deloitte's most strategic clients. You'll set technical direction, remove delivery blockers, and stay hands\-on, designing, reviewing, and debugging systems with the team. You'll translate engineering trade\-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C\-suite level, while maintaining hands\-on technical credibility, is what sets you apart. Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte's global delivery capability to maximize speed and scale.
Client Engagement
- Serve as the senior client\-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
- Lead executive\-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
- Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision
- Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements\-contributing to pipeline development and deal shaping.
Cross\-Functional Pod Leadership \& Program Governance
- Lead FDE pods of 2\-5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health
- Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates
- Coordinate multi\-pod or multi\-workstream engagements, ensuring reliable architecture and consistent client experience.
- Mentor and develop junior FDEs
GenAI Solution Development
- Architect and oversee delivery of LLM\-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below)
- Set direction for prompt engineering, tool\-use patterns, and human\-in\-the\-loop controls
- Govern end\-to\-end RAG pipeline design\-including ingestion, chunking, embedding, vector retrieval, and hybrid search\-ensuring production\-grade quality and scalability.
- Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.
Engineering \& Data Foundations
- Review and contribute to production\-quality code
- Guide architecture of data pipelines powering GenAI use cases
- Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices
- Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)
The team
AI \& Engineering leverages cutting\-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission\-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology \& data platforms. Our delivery models are tailored to meet each client's unique requirements.
Required qualifications
- Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering.
- 7\+ years of experience in software engineering, data engineering, data science, or analytics engineering.
- 1\+ years of hands\-on experience building and deploying GenAI/LLM\-powered solutions in client or production environments
- 1\+ years of experience with one of the following Frontier GenAI Platforms: Anthropic, Google or Open AI, including hands on experience with one of the following key platforms/products; Claude API, Claude for Enterprise, tool use, extended thinking, Claude Code, Gemini API, Vertex AI Agent Builder, Grounding, Google Workspace integration, GPT\-4o, Assistants API, Responses API, OpenAI Agents SDK
- 1\+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
- 1\+ years of experience building reliable, maintainable, and well\-documented code
- Ability to travel 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 cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
- Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast\-paced, ambiguous delivery environments
- Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
- Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management
- Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event\-driven architectures
- Experience operating within hybrid onshore/offshore teams
- Familiarity with security, privacy, and compliance considerations
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 $167,000 \- $307,500
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 $167K-$307K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($237K) sits 42% above the category median. Disclosed range: $167K to $307K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Deloitte AI Hiring
Deloitte has 914 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist, Data Engineer. Positions span Atlanta, GA, US, Arlington, VA, US, Tampa, FL, US. Compensation range: $110K - $311K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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