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About This Role
We are seeking an accomplished technology executive to serve as Chief Technology Officer for our Databricks Data & AI consulting practice. As the senior technical authority for Databricks and the public voice of the practice, you will guide clients through data strategy, architecture and migration to Databricks, lead the technical side of pursuits, own the Databricks relationship, and build the practice's assets and market presence.
Reporting to the CEO, you will partner with client executives, our account and delivery leaders, Databricks' field teams, and industry analysts. Success is measured by:
- Client outcomes: successful migrations and data and AI capabilities in production
- Practice growth: pipeline influenced, deals won and reusable assets in use
- Databricks channel and market presence: co-sell pipeline, partner program standing, and analyst and media visibility
Req# 1112776496
Responsibilities
- Shape Client Data & AI Strategy: Advise client executives on multi-year data and AI platform strategy, target-state architecture, governance and AI readiness, and set the practice's vision and offerings for AI modernization, agentic analytics and advanced analytics
- Set Architecture & Engineering Standards: Define reference architectures and standards for Lakehouse and medallion architecture, Unity Catalog governance, Lakeflow pipelines and Mosaic AI, together with production-grade practices for CI/CD, MLOps, FinOps and the production release cycle
- Lead Strategic Pursuits: Own the technical side of major Databricks opportunities, from discovery workshops and assessments to solution design, effort estimation, RFP responses, SOW shaping and executive presentations, and act as technical reviewer and escalation point across engagements
- Drive Legacy Modernization: Lead the design of migration programs from legacy data warehouses and ETL platforms to Databricks, and build the practice's migration toolkit of assessment templates, conversion accelerators and reconciliation frameworks
- Own the Databricks Partnership: Lead the technical relationship with Databricks, including co-selling, joint go-to-market and roadmap briefings, and grow the practice's standing in the Databricks partner program alongside AWS, Azure and GCP relationships
- Build the Practice & Market Presence: Create reusable assets and solution offerings, lead public relations and analyst relations as the practice's primary technical spokesperson, and represent EPAM at conferences, webinars and Databricks community events
Requirements
- Executive Background: 10+ years of progressive technology leadership, including 5+ years as a CTO, VP Engineering, Chief Architect or equivalent, with consulting, systems-integrator or partner-services experience and a proven ability to influence and align senior technical leaders across teams
- Deep Databricks Expertise: Hands-on experience designing and delivering enterprise-scale production systems on Databricks (Lakehouse, Delta Lake, Lakeflow, structured streaming, Unity Catalog, multi-workspace or multi-cloud deployments), with the ability to dive into the detail of every step: reviewing designs, data models, code, performance and cost
- Legacy Migration Track Record: Demonstrated success leading migrations from legacy data warehouses and ETL tools (e.g., Teradata, Netezza, Oracle, SQL Server, Informatica, SSIS) to Databricks, through assessment, wave planning, code conversion, reconciliation and cutover, with the judgement to validate estimates
- AI & Advanced Analytics: Track record of shipping production ML and generative AI systems, and experience with AI modernization, agentic analytics and advanced analytics (e.g., Agent Bricks, AI/BI Genie, RAG, predictive models) delivering measurable business impact
- Engineering Excellence: Strong command of Apache Spark internals, at least one major cloud (AWS, Azure, GCP), CI/CD with Databricks Asset Bundles, infrastructure-as-code, MLOps and FinOps, plus hands-on Python/PySpark and SQL, and experience establishing governance and security in regulated industries
- Market & Ecosystem Knowledge: Ability to position Databricks credibly against Snowflake, Microsoft Fabric, BigQuery and AWS-native analytics, and solid knowledge of interoperability (Delta Lake, Iceberg, UniForm, Delta Sharing) and the surrounding BI, orchestration and streaming ecosystem
- Certifications: A Databricks professional- or architect-level credential (e.g., Data Engineer Professional, Platform Architect accreditation), or commitment to obtain one within 6 months; ML/GenAI certifications, partner accreditations, or Databricks Champion/MVP status are a strong plus
- Executive Communication & Thought Leadership: Exceptional storytelling, presentation and executive advisory skills, with experience representing an organization to board-level stakeholders, industry analysts (e.g., Gartner, Forrester, Everest Group, ISG) and the media
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
This Remote Position Cannot be Performed in New York City.
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: $230,000 - $250,000. 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.
In accordance with the LA County Fair Chance Ordinance, you may find a copy of the Notice containing a summary of the Ordinance’s key provisions here: Concept FCO Posting 8 27 24 (lacounty.gov)
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 $230K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($240K) sits 13% above the category median. Disclosed range: $230K to $250K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
EPAM Systems AI Hiring
EPAM Systems has 24 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Consultant, MLOps Engineer. Positions span Remote, US, Houston, TX, Houston, TX, US. Compensation range: $160K - $250K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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