Interested in this AI/ML Engineer role at Genpact?
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About This Role
Chief AI EngineerReady to turn bold ideas into real\-world impact?
At Genpact, we don’t just adapt to change, we lead it. AI and digital innovation are transforming the way businesses work, and we’re at the forefront of it. Genpact’s AI Gigafactory, our industry\-first accelerator, exemplifies how we scale advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. Whether tackling complex challenges through large\-scale models or agentic AI, our breakthrough solutions tackle companies’ most complex challenges.
If you thrive in a fast\-moving, innovation\-driven environment, love building and deploying cutting\-edge AI solutions, and want to push the boundaries of what’s possible, this is your moment.
Genpact (NYSE: G) is an agentic and advanced technology solutions company. We leverage process intelligence and artificial intelligence to deliver measurable outcomes. With a strong partner ecosystem and decades of client trust, we provide innovative solutions that transform how businesses run. Powered by a team with an active learning mindset and client centricity at its core, we deliver lasting value for the world’s leading enterprises.
Get to know us at genpact.com and on LinkedIn, YouTube, X, and Facebook.
Job Description
What You Will Do
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- Lead 2–5 concurrent client engagements or one flagship transformation. Own outcomes — production adoption and measurable workflow impact — not just deliverables.
- Be the trusted technical advisor to client executives (CIO, CDO, Chief AI Officer, CFO). Strategy in the morning, working demo in the afternoon.
- Architect and build the hardest parts of the hardest problems alongside your team. We will ask you to walk us through code you have written in the last 30 days.
- Deliver production\-grade technical artifacts: agentic workflows, MCP servers, sub\-agents, agent skills, evaluation frameworks, and the integrations that make them work inside client systems.
- Grow and develop a team of Forward Deployed Engineers. Recruit, coach, set the bar, and create the environment where strong engineers do the best work of their careers.
- Shape Genpact's Applied AI offerings, reusable IP, and go\-to\-market. Codify what works on one engagement into patterns the whole practice can reuse.
- Partner with Anthropic and other strategic AI partners on co\-sell motions, joint solutioning, and customer success.
What We Are Looking For
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- Experience in software engineering, applied AI, data, or product, with clear evidence of leading technical work and growing other builders.
- Has been the primary architect on at least one AI, data, or software effort that shipped to real users at meaningful scale.
- Daily, sophisticated working fluency with Claude (or comparable frontier LLM) as a primary build tool. You have opinions about context engineering, tool use, agent design, and evaluations — and you can defend them.
- Production LLM experience: prompt engineering, agent development, evaluation frameworks, and deployment at scale.
- Strong programming skills in Python, ideally with a second language such as TypeScript or Java. Comfortable shipping production code.
- Executive presence. Can run a room with a CIO and a CFO without needing a slide deck to hide behind. Can translate technical complexity into clear narratives for diverse audiences.
- Track record of hiring and developing strong technical talent. People who have worked for you want to work for you again.
- High agency. Thrives in ambiguity and is energized by building something that does not yet exist.
Qualifications
Certifications
Required Skills
AI/ML OpsLanguage
English (Required)Language Proficiency \-
Proficient \- C2Additional Job Location \-
Job Type
RegularMaster Skill List \-
Advanced Analytics / AI / MLRemote Type \-
HybridWork Shift \-
Any (United States of America)The approximate annual base compensation range for this position is:
165,300 to 235,000 USD
“Los Angeles California\-based candidates are not eligible for this role”The actual offer, reflecting the total compensation package plus benefits, will be determined by a number of factors which include but are not limited to the applicant’s experience, knowledge, skills, and abilities; geographic location; and internal equity.
Why join Genpact?
- Lead AI\-powered transformation – Drive innovation and solve real\-world business challenges that matter
- Make an impact – Help global enterprises solve business challenges that matter
- Accelerate your career – Gain hands\-on experience, mentorship, and world\-class learning opportunities to stay ahead
- Work with the best – Join 140,000\+ bold thinkers and problem\-solvers who push boundaries every day
- Thrive in a values\-driven culture – Our courage, curiosity, and incisiveness \- built on a foundation of integrity and inclusion \- allow your ideas to fuel progress
Come join the 140,000\+ coders, tech shapers, and growth makers at Genpact and take your career in the only direction that matters: Up.
Let’s build tomorrow together.
Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation.
Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a 'starter kit,' paying to apply, or purchasing equipment or training.
Salary Context
This $165K-$235K range is above 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 Genpact, 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. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $165K to $235K.
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.
Genpact AI Hiring
Genpact has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Atlanta, GA, US, Avenel, NJ, US, New York, NY, US. Compensation range: $62K - $235K.
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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