Senior AI Engineer/ A2A, Databricks, MCP

EPAM Systems; Turkey; On-site; TRY 5,932,000 - 8,403,000; Full time

Benefits offered by this job

Private health insurance

English courses

Competitive USD pay

Job summary

EPAM Systems in Turkey is 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.

You will design and implement core AI agents from specification through production deployment, ensuring accurate, reliable, and fully integrated solutions within the platform's shared services.

Qualifications

• 3+ years of experience building LLM-based applications in Python.
• Hands-on experience with RAG pipelines (retrieval, chunking, embedding, reranking, generation).
• Proficiency in prompt engineering (system prompts, few-shot patterns, output structuring).
• Experience with LLM orchestration frameworks (LangChain, LlamaIndex or equivalent).
• Familiarity with evaluation techniques (LLM-as-judge, RAG faithfulness).
• Expertise in Databricks and MLflow including experiment tracking and serving.
• Knowledge of MCP or similar tool-use patterns.
• Experience with vector stores (Chroma, Pinecone, Databricks Vector Search).
• REST API integration and OAuth authentication flows.
• Strong product thinking and communication with non-technical stakeholders.
• English at a B2+ level.

Responsibilities

• Participate in use case sessions and translate requirements into specifications using a multi-factor framework.
• Design and implement RAG pipelines for document retrieval, embedding, and generation.
• Integrate agents with MCP servers for tool access and with LLM Gateway for routing.
• Apply prompt engineering practices and iterate based on evaluations.
• Wire guardrails for reference agents and guard against prompt injection.
• Run evaluation cycles using the platform framework and improve quality gates.
• Collaborate with Data Engineer on data schema and retrieval interfaces; with QA on test coverage.
• Support UAT with business stakeholders and prepare agents for production deployment.

Skills

Python

LLM integration

RAG pipelines

Prompt engineering

LangChain

LlamaIndex

Vector stores

REST APIs

OAuth

English (B2+)

Tools

Databricks

MLflow

LangChain

LlamaIndex

Chroma

Pinecone

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.

Responsibilities

• Participate 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 definition
• Design and implement the RAG pipeline, including document retrieval, chunking strategy, embedding, reranking, and response generation
• Integrate agents with platform MCP servers for tool access and with the LLM Gateway for model routing
• Implement prompt engineering practices such as system prompts, few‑shot examples, and chain‑of‑thought patterns, and iterate based on evaluation results
• Wire guardrails, including prompt injection protection and domain boundary enforcement, for reference agents
• Run 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 met
• Collaborate with the Data Engineer on data schema and retrieval interface design, and with the QA Engineer on test coverage and the acceptance test query set
• Support UAT with business stakeholders, address feedback, and prepare agents for production deployment following the platform runbook

Requirements

• 3+ years of experience building LLM-based applications in Python
• Hands‑on experience 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
• Strong product thinking, with the ability to connect technical implementation choices to user‑facing quality outcomes
• Iterative mindset, comfortable with repeated evaluation‑tune‑evaluate cycles without losing focus on delivery deadlines
• Clear communication with non‑technical business stakeholders during UAT and discovery sessions
• Proficiency in English at a B2+ level

Nice to have

• Prior delivery of a production RAG or agentic AI system end-to-end
• Experience working within a governed AI platform, such as an LLM gateway, guardrails, and evaluation framework, rather than fully custom stacks
• Familiarity with responsible AI concepts, including hallucination, grounding, PII, and prompt injection

We offer

• CONTINUOUS UPSKILLING, LEARNING & DEVELOPMENT
  • Diversity of tasks and projects
  • Assessment center for objective review of competency level
  • Personal development plan
  • Mentoring programs and leadership development
  • Certification and professional development support
  • Access to learning platforms including more than 2,500 internal courses
  • English courses taught by certified teachers
• CORPORATE BENEFITS
  • Extra leave days
  • Referral bonuses
• COMPENSATION PACKAGE
  • Competitive compensation paid in USD
  • Regular salary and performance reviews
• MEDICAL & HEALTHCARE
  • Private health insurance
  • Well‑being events
• WORKING ENVIRONMENT
  • Recreation areas and kitchens
  • Tea, coffee and snacks
  • Sports equipment and game consoles
  • IT Equipment
  • Microsoft’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.