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About This Role
EPAM Systems is a leading global provider of digital platform engineering and software development services. We help global enterprises innovate, build and transform their core businesses through technology.
Join our AI\-Centric Delivery practice as a Solutions Architect. You will design and deliver enterprise solution architectures where AI serves as a foundational engineering capability. Combine your architectural expertise with hands\-on application to build AI\-augmented software development lifecycle (SDLC) workflows, agentic systems and LLM\-powered delivery tooling.
This execution\-oriented role empowers you to define technical direction, validate architectures through personal prototyping and work alongside engineering teams during implementation to drive real impact.
Responsibilities
- Design, build and validate AI\-SDLC developer agents and multi\-agent orchestration workflows focusing on automation and engineering throughput
- Advise senior client stakeholders by translating business requirements into AI\-augmented solution architectures
- Communicate design trade\-offs across latency, cost, observability and risk
- Architect and integrate AI\-enabled workflows across the engineering stack, including version control, CI/CD pipelines, code review, testing and documentation
- Deliver functional prototypes within tight delivery windows to demonstrate the value of AI\-native engineering approaches
- Lead the end\-to\-end design of enterprise solution architectures incorporating agentic systems, LLM\-powered workflows and RAG pipelines
- Collaborate with engineering leads, product owners and enterprise architecture teams to align solution designs with security, governance and integration requirements
Requirements
- Proven track record as a senior software engineer or solutions architect with successful delivery across complex enterprise\-scale engagements
- Hands\-on expertise with large language models and generative AI (such as Anthropic Claude, OpenAI GPT or Google Gemini)
- Demonstrated capability in prompt engineering, model selection, context management and cost and latency optimization in production environments
- Background in designing and implementing agentic workflows involving tool use, memory systems, multi\-step reasoning and human\-in\-the\-loop patterns
- Solid foundation in enterprise architecture fundamentals, including cloud platforms (AWS, Azure or GCP), microservices, API design, data architecture and integration patterns
- Clear communication practices for navigating strategic design and hands\-on implementation to validate architectural decisions through working prototypes
Nice to have
- Familiarity with multi\-agent orchestration frameworks like CrewAI, AutoGen or LangGraph
- Knowledge of LLM evaluation, guardrails and observability tooling like LangSmith or Arize
- Practical understanding of AI development frameworks, including LangChain, LlamaIndex or Hugging Face
- Exposure to vector database technologies like FAISS, Pinecone, Qdrant, Chroma or Weaviate
- Experience deploying AI\-assisted code generation tooling at an organizational scale
- Background in enterprise integration platforms, event\-driven architecture or data mesh
We offer
- By choosing EPAM, you're getting a job at Great Place To Work\-Certified™ in 2024, Glassdoor’s Top 100 Best Places to Work in 2023 \& one of Most Loved Workplace, as recognized by Newsweek, 2021 \- 2023\.
- Your voice matters. At EPAM, employee ideas are the driving force behind our business. You'll be part of a supportive workplace where everyone is respected and included.
- Through extensive opportunities for internal training and self\-development, including workshops, online courses, and mentoring programs, you'll learn, contribute, and grow with us.
- You'll be challenged while working side\-by\-side with some of the best talent globally. We work with top\-notch technologies, constantly seeking new industry trends and best practices.
Life at EPAM
- EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees and our communities
- Since EPAM Japan was incorporated in 2018, we have been constantly expanding our team and capabilities in Tokyo, the capital of the world's third\-largest economy
- We have top\-notch multilingual specialists who are experts in consulting, designing, and engineering to achieve digital transformation for businesses, especially in financial services, life science, automotive, real estate, and retail
- With a proactive, creative, diverse team, we offer our clients solutions that envision digitalization in their systems, products, and services. We are here to make an impact in our community and beyond
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
EPAM Systems AI Hiring
EPAM Systems has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span New York, NY, US, Remote, US, San Francisco, CA, US. Compensation range: $240K - $400K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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