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EPAM
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Senior AI Engineer

EPAMArmenia; Georgia; Kazakhstan; Kyrgyzstan; Uzbekistan
Work model
Hybrid
Experience
5+ years
Employment
Not specified
Compensation
Not disclosed
Technology signal
26 tags

Technology context

26

Parsed from the vacancy text; ordered by relevance to this role.

AI AgentsAILLMAWSJavaPythonRAGGCPKubernetesCloud NativeCloudGoC++DockerMicroservicesAPISRECI/CDHelmAutomationAI Agents DevelopmentLangChainLlamaIndexSearch Engine OptimizationStorage SystemsVector Databases

Full listing

Role description

We are seeking a Senior AI Engineer to design, deploy and optimize cutting-edge AI infrastructure powering large-scale GenAI applications. In this role, you will work with vector databases, LLM frameworks and cloud-native technologies to build robust, production-grade systems that drive intelligent solutions across the organization.

Responsibilities

  • Deploy and manage Milvus vector databases including schema design and index tuning with HNSW and IVF-FLAT
  • Build embedding and LLM framework pipelines leveraging OpenAI API, Hugging Face or Cohere
  • Manage Kubernetes clusters, Helm charts and containerized microservices for scalable orchestration
  • Implement Docker containerization with multi-stage builds and registry management
  • Develop production-level applications in Python along with Go, Java or C++
  • Integrate object storage systems including AWS S3, MinIO or Google Cloud Storage
  • Support large-scale RAG applications and multi-agent platforms
  • Optimize compute and inference through GPU scheduling, resource optimization and inference acceleration
  • Drive search optimization with hybrid search, metadata filtering and index tuning
  • Collaborate effectively with the team to deliver high-quality solutions

Requirements

  • B.Tech/B.E in Engineering with 5+ years of relevant experience
  • Expertise in Milvus deployment, schema design and index tuning (HNSW, IVF-FLAT)
  • Familiarity with Qdrant, Pinecone, Weaviate, PGVector or Chroma
  • Proficiency in OpenAI API, Hugging Face or Cohere for embeddings and LLMs
  • Skills in Kubernetes cluster management, Helm charts and containerized microservices
  • Competency in Docker containerization, multi-stage builds and registry management
  • Production-level proficiency in Python along with Go, Java or C++
  • Knowledge of object storage integration including AWS S3, MinIO or Google Cloud Storage
  • Excellent verbal and written communication skills

Nice to have

  • Background in supporting large-scale RAG applications and multi-agent platforms
  • Familiarity with LangChain, LlamaIndex or custom LLM orchestration pipelines
  • Understanding of AI observability through LLM evaluation, governance, tracing and monitoring tools
  • Knowledge of CI/CD pipelines, Infrastructure-as-Code and cloud-native deployment practices
  • Prior work experience in the Oil and Gas industry along with Dataiku DSS and SRE practices