Interested in this AI/ML Engineer role at EPAM Systems?
Apply Now →About This Role
This is a hands-on, in-house marketing role for someone who enjoys teaching, building and experimenting.
As the Marketing Enablement & AI Adoption Specialist at EPAM, you'll own a continuous global marketing enablement program for our employer branding and recruitment marketing teams across EPAM's regions. You'll spot capability gaps, create practical training and drive generative AI adoption by turning new tools into repeatable practices teams can use every day.
You'll also drive the evolution of TAMI, our AI-enabled recruitment marketing assistant, and the TAM Hub, our global knowledge and enablement home. As part of our content governance model, you'll serve as a backup reviewer for external talent marketing content. In the near term, you'll also support select Marketing Operations & Content Coordination work, such as presentations and resource management.
EPAM's Global Talent Marketing & Candidate Engagement team positions EPAM as an employer of choice across the 55+ countries and regions where we operate. We activate and protect our global Employer Value Proposition and employer brand, and we build an integrated, data-informed talent marketing ecosystem. Through our Center of Excellence model, we give regional Talent Attraction teams the strategy, tools, standards and best practices to execute consistently and at scale.
Req.#1094011004
Responsibilities
- Own the global Talent Marketing training and enablement program, identifying capability gaps across regional teams and turning them into targeted training plans
- Design and facilitate training sessions, workshops and demos for a global audience
- Create playbooks, templates, toolkits and self-service resources that help marketers apply best practices consistently
- Manage the TAM Hub day to day as the central home for global Talent Marketing knowledge and tools
- Own the evolution and adoption of TAMI, identifying new use cases and improving how marketers use it
- Help shape the AI and innovation roadmap: evaluate emerging tools, run practical pilots and turn manual workflows into scalable solutions
- Drive AI adoption through communications, coaching and reinforcement, and measure training effectiveness, usage and productivity to keep improving
- Serve as a backup reviewer for external talent marketing content, checking grammar, clarity, tone and brand consistency
- Build polished PowerPoint and other materials for training, leadership and global team communications
- Support resource libraries and other marketing operations work, and help transition routine tasks to dedicated Marketing Operations & Content Coordination support
Requirements
- 5+ years in marketing enablement, learning and development, employer branding, recruitment marketing, digital marketing, AI adoption or marketing operations
- Experience designing and delivering training or enablement programs for professional audiences, with strong facilitation skills and the ability to make complex topics clear
- Experience creating playbooks, guides, templates and self-service resources
- Hands-on experience applying generative AI to real marketing or business workflows, including testing new tools and helping others adopt them
- Track record of driving adoption of new tools, processes or ways of working
- Excellent writing and copyediting skills with a strong command of U.S. English, and the ability to give clear, constructive feedback on external-facing content
- Advanced PowerPoint skills and working knowledge of Canva or similar tools Strong project management skills, with the ability to balance recurring programs and emerging priorities
- Ability to communicate and facilitate across cultures, regions and levels of seniority; experience in a global, distributed or Center of Excellence environment preferred
- Bachelor's degree in Marketing, Communications, Business, Learning & Development or a related field
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: $70,000 - $90,000. 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 $70K-$90K range is in the lower quartile 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($80K) sits 62% below the category median. Disclosed range: $70K to $90K.
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.