Overview

We’re seeking a Senior AI Engineer to architect and ship LLM-powered applications and agentic systems that address genuine challenges for our users and teams. Your work will span the entire applied-AI stack — from retrieval-augmented generation (RAG) pipelines and prompt design to multi-step agents that reason, leverage tools, and automate end-to-end workflows.

This position is for a senior builder. You’ll drive complex use cases from ambiguous problems through to production: selecting appropriate models, implementing agentic architectures, connecting retrieval and tooling, establishing quality evaluation methods, and delivering dependable applications at scale.

Responsibilities:
  • Architect and deliver production LLM applications — chat, copilots, assistants, and autonomous workflows — from idea to scale
  • Construct and implement RAG pipelines: chunking, embeddings, vector search, reranking, and grounding to minimize hallucination and boost relevance
  • Create agentic architectures: multi-step reasoning, tool/function calling, planning, memory, and multi-agent orchestration
  • Support the automation of business and engineering workflows through agentic AI and workflow automation
  • Establish prompt and context strategies; construct evaluation harnesses and maintain quality, latency, and cost standards
  • Connect LLMs with internal data, APIs, and tools through connectors, function calling, and structured outputs
  • Deploy guardrails, safety, and observability for AI systems (tracing, evals, monitoring for quality and drift)
  • Partner with product, data, and platform teams to transform ambiguous problems into shipped AI features
  • Exchange knowledge with fellow engineers and take part in design reviews
Required Qualifications:
  • 3+ years in software or ML engineering, including recent, hands-on experience building and shipping applications with LLMs
  • Demonstrated track record of delivering applied-AI systems end to end
  • Proficiency in Python at an advanced level
  • Background in building RAG systems — embeddings, retrieval, and reranking with vector databases (Pinecone, Qdrant, Milvus, or pgvector)
  • Expertise in LLM APIs and orchestration frameworks (OpenAI, Anthropic, LangChain, or LlamaIndex)
  • Skills in designing agentic architectures — tool use, function calling, planning loops, and agent orchestration in production
  • Competency in automating workflows with agentic AI or workflow-automation tooling
  • Knowledge of prompt engineering and structured/JSON output techniques
  • Capability to design evaluations and reason about LLM quality, cost, and latency trade-offs at scale
  • Strong command of written and spoken English (B2+ level)
Benefits:
  • Delivering innovative solutions to industry leaders, making a global impact
  • Enjoyable working environment, whether it is the vibrant office or the comfort of your home
  • Opportunity to work abroad for up to two months per year
  • Relocation opportunities within our offices in 55+ countries
  • Corporate and social events
  • Leadership development, career advising, soft skills and well-being programs
  • Certifications, including GCP, Azure and AWS
  • Unlimited access to EPAM's internal learning database
  • Free English classes with certified teachers
  • Participation in the Employee Stock Purchase Plan
  • Monetary bonuses for engaging in the referral program
  • Comprehensive medical & family care package
  • Four trust days per year for personal needs
  • Discounts for fitness clubs
  • Benefits package (hotels, restaurants, stores and services)
Nice To Have:
  • Familiarity with multi-agent frameworks (LangGraph, CrewAI, or AutoGen)
  • Background in fine-tuning, adapters (LoRA), or model distillation
  • Understanding of MLOps/LLMOps — deployment, versioning, and monitoring of AI systems, including model serving and inference optimization
  • Knowledge of AI safety, guardrails, and evaluation frameworks (Ragas, LangSmith, or promptfoo)
  • Expertise in cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
Skills:
  • AI Solution Engineering
Note:

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