Senior AI Engineer/ A2A, Databricks, MCP

EPAM Systems, Inc.; Turkey; On-site; TRY 2,457,000 - 4,423,000; Full time

Benefits offered by this job

Learning & Development

Mentoring programs

Private health insurance

English courses

Career growth

Job summary

EPAM Systems, Inc. seeks a Senior AI Engineer to join an enterprise AI platform engineering initiative.

You will design and implement core AI agents on a Databricks-native platform, ensuring production-ready deployment with robust guardrails and evaluation-driven quality gates. You will collaborate across data, QA, and business teams to translate requirements into scalable, end-to-end AI solutions, delivering capabilities across GCP and Azure environments.

Qualifications

• 3+ years of experience building LLM-based applications in Python.
• Hands-on with RAG pipelines, covering retrieval, chunking, embedding, reranking, and generation.
• Proficiency in prompt engineering, including system prompt design, few-shot patterns, and output structuring.
• Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or equivalent.
• Familiarity with LLM evaluation techniques, including LLM-as-judge, RAG faithfulness metrics, and RAGAS or equivalent.
• Expertise in Databricks and MLflow, including experiment tracking and model serving awareness.
• Knowledge of MCP (Model Context Protocol) or equivalent tool-use / function-calling patterns.
• Experience with vector stores such as Chroma, Pinecone, or Databricks Vector Search, or equivalent.
• Skills in REST API integration and OAuth authentication flows.

Responsibilities

• Participate in use case deep-dive sessions and translate requirements into specifications.
• Design and implement the RAG pipeline including document retrieval, embedding, and reranking.
• Integrate agents with platform MCP servers and LLM Gateway for routing.
• Implement prompt engineering practices and iterate based on evaluation results.
• Wire guardrails and domain boundaries for reference agents.
• Run evaluation cycles using the platform Evaluation Framework and improve quality gates.
• Collaborate with Data Engineer on data schema and retrieval interface design; with QA on test coverage.
• Support UAT with business stakeholders and prepare agents for production deployment.

Skills

Python

LLM-based apps

RAG pipelines

Prompt engineering

LangChain

LlamaIndex

Databricks

MLflow

MCP

Vector stores

REST API

OAuth

English (B2+)

Tools

Databricks

MLflow

LangChain

LlamaIndex

Chroma

Pinecone

Databricks Vector Search

Job description

We are seeking a Senior AI Engineer to join an enterprise AI platform engineering initiative delivering a Databricks-native, MCP-first platform with a Hub & Spoke governance model, enabling independent spoke teams to build and deploy AI agents across GCP and Azure environments. In this role, you will design and implement core AI agents on the platform, from specification through production-ready deployment, ensuring accurate, reliable, and fully integrated solutions within the platform's shared services.ResponsibilitiesParticipate in use case deep-dive sessions and translate business requirements into specifications using a 15-characteristic framework covering scope, tools, memory, guardrails, evaluation criteria, and acceptance definitionDesign and implement the RAG pipeline, including document retrieval, chunking strategy, embedding, reranking, and response generationIntegrate agents with platform MCP servers for tool access and with the LLM Gateway for model routingImplement prompt engineering practices such as system prompts, few-shot examples, and chain-of-thought patterns, and iterate based on evaluation resultsWire guardrails, including prompt injection protection and domain boundary enforcement, for reference agentsRun evaluation cycles using the platform Evaluation Framework, including LLM-as-judge scoring and RAG faithfulness and relevance metrics, and iterate until quality gate criteria are metCollaborate with the Data Engineer on data schema and retrieval interface design, and with the QA Engineer on test coverage and the acceptance test query setSupport UAT with business stakeholders, address feedback, and prepare agents for production deployment following the platform runbookRequirements3+ years of experience building LLM-based applications in PythonHands-on experience with RAG pipelines, covering retrieval, chunking, embedding, reranking, and generationProficiency in prompt engineering, including system prompt design, few-shot patterns, and output structuringExperience with LLM orchestration frameworks such as LangChain, LlamaIndex, or equivalentFamiliarity with LLM evaluation techniques, including LLM-as-judge, RAG faithfulness metrics, and RAGAS or equivalentExpertise in Databricks and MLflow, including experiment tracking and model serving awarenessKnowledge of MCP (Model Context Protocol) or equivalent tool-use / function-calling patternsExperience with vector stores such as Chroma, Pinecone, or Databricks Vector Search, or equivalentSkills in REST API integration and OAuth authentication flowsStrong product thinking, with the ability to connect technical implementation choices to user-facing quality outcomesIterative mindset, comfortable with repeated evaluation-tune-evaluate cycles without losing focus on delivery deadlinesClear communication with non-technical business stakeholders during UAT and discovery sessionsProficiency in English at a B2+ levelNice to havePrior delivery of a production RAG or agentic AI system end-to-endExperience working within a governed AI platform, such as an LLM gateway, guardrails, and evaluation framework, rather than fully custom stacksFamiliarity with responsible AI concepts, including hallucination, grounding, PII, and prompt injectionWe offerCONTINUOUS UPSKILLING, LEARNING & DEVELOPMENTDiversity of tasks and projectsAssessment center for objective review of competency levelPersonal development planMentoring programs and leadership developmentCertification and professional development supportAccess to learning platforms including more than 2,500 internal coursesEnglish courses taught by certified teachersCORPORATE BENEFITSExtra leave daysReferral bonusesCOMPENSATION PACKAGECompetitive compensation paid in USDRegular salary and performance reviewsMEDICAL & HEALTHCAREPrivate health insuranceWell-being eventsWORKING ENVIRONMENTRecreation areas and kitchensTea, coffee and snacksSports equipment and game consolesIT EquipmentMicrosoft’s Software Assurance Home Use Program (HUP)Please note that our Talent Attraction Team reviews applications and CVs submitted in English.EPAM is global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and ambitious startups. With over thirty years of expertise in custom software, product and platform engineering, we empower our clients to become AI-Native enterprises, driving measurable value from innovation and digital investments.