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# AI Engineer

- **Company:** EPAM Systems, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Continuous Integration, Graph Database, Python (Programming Language), Azure Active Directory, Azure Machine Learning, TypeScript, Data Classification, Large Language Models, Spring-boot, AngularJS, Kubernetes, Azure Service Fabric
- **Published:** July 31, 2026
- **Apply:** https://www.dice.com/job-detail/f0b9168b-2f95-44e2-a9b7-1e4267a80003

## About the Role

We are looking for a Senior AI Engineer with Microsoft Azure expertise to take validated prototypes and make them survive production: evals, guardrails, security, cost and scale. You will build agentic systems on the Azure stack and code AI-first every day, with the commits to prove it. We value an 80% mindset and 20% skills approach - frameworks change quarterly, and we don't hire for one. What we can't teach is evaluation-driven engineering discipline and the honesty to say what a demo hides. Responsibilities Industrialize prototypes into production services on Azure - Azure AI Foundry / Azure OpenAI - from build-ready pack to a system real users depend on in weeks Build agentic systems properly, choosing orchestration frameworks, RAG pipelines, vector DBs, knowledge graphs and MCP-based tool integration by need and engineer them for change Build the eval harness first: golden sets, regression evals and guardrail tests wired into CI, with quality measured on every change Engineer
the guardrails, including input/output filtering, grounding and citation, PII protection, rate limits and human escalation paths Deliver full stack services in Python and/or Java Spring Boot along with TypeScript/Angular front ends Run production engineering end-to-end, covering CI/CD, observability with traces on every LLM call, cost and latency management and model-version churn absorbed by design Build security and compliance in, respecting data classification boundaries in prompts, stores and logs, externalizing secrets and making every AI decision auditable Iterate from real usage through hypercare, tuning and fixes based on evidence, and package patterns that worked for the next pod Requirements 3+ years of experience shipping LLM/agentic systems in production with real users, with a defined eval approach and scale Proficiency in Python, Java Spring Boot and/or TypeScript/Angular Expertise in Azure PaaS and Azure AI services Skills in agentic frameworks, vector DBs, knowledge graphs
and MCP Competency in prompt and context engineering Background in CI/CD and observability, having operated what you built Familiarity with daily AI-assisted engineering, coding with AI agents and demonstrating the workflow live English proficiency at B2 level or higher



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