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About This Role
EPAM Systems is a global team of technologists who design, develop and deliver software that powers the world. We are at a pivotal moment where AI is the core engine of our engineering craft.
Join our AI\-Centric Delivery team as a Senior Software Engineer to redefine the Software Development Lifecycle (SDLC). Go beyond writing code to design and orchestrate AI agents and agentic workflows that accelerate development velocity.
If you are a tool\-agnostic engineer focused on the future of AI\-native engineering, this role offers the opportunity to build, test and ship transformative technical solutions for global brands.
Responsibilities
- Design and build sophisticated AI developer agents capable of generation, refactoring and documentation of complex codebases at scale
- Champion the shift from manual coding to an agentic workforce while maintenance of hands\-on technical involvement for high\-level precision
- Deliver functional, high\-velocity prototypes in tight windows to prove the real\-world power of AI\-driven engineering
- Design and integrate AI\-enabled workflows directly into the engineering stack, including Git, Jira and CI/CD pipelines, to automate processes and maximize developer throughput
- Consult with stakeholders to translate complex business requirements into AI\-SDLC\-augmented technical solutions
- Articulate the trade\-offs of agentic design and ensure alignment with enterprise goals
Requirements
- Extensive engineering background as a high\-performing developer with mastery of Java, JavaScript, Python or .NET
- Hands\-on expertise with large language models including Anthropic Claude, OpenAI GPT or Google Gemini
- Proven capability in development of agentic workflows featured with tool use, memory and multi\-step reasoning focused on engineering tasks
- Strong understanding of RAG architectures, prompt engineering and the mechanics of AI integration into production\-grade SDLC workflows
- Execution\-focused mindset with a strong preference for build, test and shipment of working solutions
Nice to have
- Experience with multi\-agent orchestration frameworks like CrewAI or AutoGen
- Knowledge of vector databases such as FAISS, Pinecone, Qdrant or Chroma
- Familiarity with LLM evaluation, guardrails and observability
- Cloud deployment experience with AWS, Azure or GCP
- Practical experience with AI frameworks such as LangChain, Hugging Face or LlamaIndex
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 Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At EPAM Systems, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $234,620 based on 682 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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.
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