Interested in this AI/ML Engineer role at EPAM Systems?
Apply Now →Skills & Technologies
About This Role
We are seeking a highly accomplished Portfolio Delivery Director - AI Native Engineering & Hyperfactory Transformation to lead one of the strategic business portfolios within a large-scale transformation engagement for a leading insurance provider.
This leader will own end-to-end accountability for portfolio delivery across multiple programs, projects, products, and managed services teams. The role is responsible for successful transition from incumbent vendors, knowledge transfer, stabilization, technology transformation, operational excellence, SLA achievement, customer satisfaction, and business outcomes. A key expectation of this role is to establish and operationalize an AI-Native Delivery Organization from Day One by implementing an AI Hyperfactory operating model, transforming traditional engineering teams into AI-augmented, high-performing delivery organizations that deliver measurable gains in productivity, quality, speed, and business value.
About EPAM Hyperfactory
EPAM's AI Hyperfactory is an AI-native delivery operating model that combines AI agents, reusable AI factories, intelligent automation, governance, security, and AI-native delivery teams to transform how software and technology services are delivered.
By embedding AI across the entire delivery lifecycle, from planning and engineering to testing, operations, and service management, Hyperfactory enables organizations to accelerate delivery, improve quality, optimize costs, and achieve measurable business outcomes while maintaining enterprise-grade control, compliance, and transparency.
This role will be responsible for driving Hyperfactory adoption across the assigned portfolio and ensuring AI-native delivery practices become foundational to how teams operate from transition through steady-state delivery.
Responsibilities
- Own end-to-end delivery of a strategic portfolio consisting of multiple programs, projects, products, and managed services engagements
- Serve as the single point of accountability for portfolio performance, delivery excellence, customer satisfaction, and business outcomes
- Drive operational excellence, continuous improvement, and delivery predictability across the portfolio
- Lead transition activities from incumbent vendors and legacy delivery organizations, including migration, stabilization, and Knowledge Transfer (KT) plans with minimal business disruption
- Establish transition governance, risk management, and executive reporting mechanisms
- Successfully transition services into steady-state operations while meeting agreed stabilization targets
- Establish and operationalize an AI Hyperfactory delivery model and define the portfolio's AI-native transformation roadmap
- Drive adoption of AI across software engineering, quality engineering, testing, release management, operations, support, and delivery governance
- Implement AI-enabled engineering practices including code generation, code reviews, automated testing, intelligent operations, knowledge management, and delivery analytics
- Build reusable AI accelerators, frameworks, playbooks, and best practices that can be scaled across the portfolio
- Drive measurable improvements in engineering productivity, delivery velocity, quality, and operational efficiency through AI adoption
- Lead organizational change and transformation of traditional teams into AI-first engineering organizations
- Monitor portfolio health, dependencies, risks, issues, and mitigation plans
- Own achievement of all SLAs, KPIs, OLAs, and contractual service commitments
- Lead executive escalations and resolution of critical delivery issues
- Build trusted relationships with client executives, business leaders, and technology stakeholders
- Lead steering committees, governance reviews, and executive business reviews
- Partner with client leadership to align delivery priorities with strategic business goals and transformation initiatives
- Own portfolio financial performance, including revenue, margin, utilization, forecasting, and cost optimization
- Support account growth, renewals, transformation opportunities, and strategic initiatives
- Build, lead, and scale high-performing global delivery teams, driving talent development, succession planning, and leadership growth
- Foster a culture of accountability, innovation, collaboration, and continuous learning
Requirements
- Bachelor's degree in Engineering, Computer Science, Information Systems, Business, or a related field
- 15+ years of experience in technology delivery, program management, managed services, or digital transformation leadership
- 8+ years of experience leading large-scale portfolios, programs, or enterprise client engagements
- Proven experience managing multi-million-dollar portfolios with accountability for delivery, customer outcomes, and financial performance
- Demonstrated success leading complex transitions from incumbent vendors, including knowledge transfer, stabilization, and operational transformation
- Strong experience operating within outcome-based, SLA-driven, or managed services delivery environments
- Experience leading globally distributed teams across onshore, nearshore, and offshore delivery models
- Deep understanding of Agile, DevOps, Product Engineering, Cloud, and modern software delivery practices
- Strong executive stakeholder management, governance, and communication skills
- Proven experience leading enterprise-scale AI Native Engineering transformations
- Demonstrated experience establishing and operationalizing AI Hyperfactory or AI-powered delivery operating models
- Experience implementing AI-enabled software engineering, testing, operations, service management, and delivery governance practices
- Track record of driving AI adoption and achieving measurable improvements in productivity, quality, speed-to-market, and operational efficiency
- Strong understanding of Generative AI, AI agents, intelligent automation, responsible AI, governance, and enterprise AI operating models
Nice to have
- Experience within Insurance, Financial Services, Banking, or other highly regulated industries
- Experience leading large-scale application modernization, digital transformation, cloud transformation, and managed services programs
- PMP, SAFe, PMI-ACP, Scrum, ITIL, or equivalent certifications
- Experience with Microsoft Copilot, Azure AI, GenAI platforms, Agentic AI frameworks, and modern AI-enabled engineering ecosystems
We offer
- Medical, Dental and Vision Insurance (Subsidized)
- Health Savings Account
- Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
- Short-Term and Long-Term Disability (Company Provided)
- Life and AD&D Insurance (Company Provided)
- Employee Assistance Program
- Unlimited access to LinkedIn learning solutions
- Matched 401(k) Retirement Savings Plan
- Paid Time Off – the employee will be eligible to accrue 15-25 paid days, depending on specific level and tenure with EPAM (accrual eligibility may change over time)
- Paid Holidays - nine (9) total per year
- Legal Plan and Identity Theft Protection
- Accident Insurance
- Employee Discounts
- Pet Insurance
- Employee Stock Purchase Program
- If otherwise eligible, participation in the discretionary annual bonus program
- If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program
For remote work in New York City only.
EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our clients, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.
Engineer the Future with a Career at EPAM
This posting includes a good faith range of the salary EPAM would reasonably expect to pay the selected candidate. The range provided reflects base salary only. Individual compensation offers within the range are based on a variety of factors, including, but not limited to: geographic location, experience, credentials, education, training; the demand for the role; and overall business and labor market considerations. Most candidates are hired at a salary within the range disclosed. Salary range: $130,000 - $205,000. In addition, the details highlighted in this job posting above are a general description of all other expected benefits and compensation for the position.
Applications will be accepted on a rolling basis.
EPAM will not provide new H-1B visa sponsorship for this position. Candidates with existing transferable H-1B status may be considered.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $130K-$205K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At EPAM Systems, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $130K to $205K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
EPAM Systems AI Hiring
EPAM Systems has 14 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Consultant, AI Software Engineer. Positions span Remote, US, Houston, TX, US, New York, NY, US. Compensation range: $90K - $260K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.