ai engineer for vector search services

EPAM develops enterprise software products, open source solutions, and technology accelerators.

• Deploy and manage Milvus vector databases, including schema design and index tuning with HNSW and IVF-FLAT;
• Build and maintain embedding and LLM pipelines using OpenAI API, Hugging Face, or Cohere;
• Manage Kubernetes clusters, Helm charts, and containerized microservices in production;
• Develop and maintain Docker containerization workflows, including multi-stage builds and registry management;
• Design and deliver production-grade Python applications, integrating with Go, Java, or C++ where required;
• Integrate object storage systems such as AWS S3, MinIO, or Google Cloud Storage;
• Evaluate and implement alternative vector database solutions, including Qdrant, Pinecone, and Weaviate;
• Collaborate cross-functionally with team members to deliver reliable, scalable AI services;
• Ensure operational excellence, observability, and performance of deployed AI workloads.

• Bachelor's degree in Engineering and 5+ years of relevant experience;
• Expertise in Milvus deployment, schema design, and index tuning with HNSW and IVF-FLAT;
• Familiarity with vector database alternatives such as Qdrant, Pinecone, Weaviate, PGVector, or Chroma;
• Proficiency in building embedding and LLM pipelines using OpenAI API, Hugging Face, or Cohere;
• Skills in Kubernetes cluster management, Helm charts, and containerized microservices;
• Background in Docker containerization, multi-stage builds, and registry management;
• Production-level Python development 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 with strong team collaboration abilities;
• English proficiency at Upper-Intermediate level (B2) or higher;
• Nice to have: experience supporting large-scale RAG applications and multi-agent platforms, hands-on familiarity with LangChain, LlamaIndex, or custom pipelines, understanding of GPU scheduling, resource optimization, and inference acceleration, production experience with hybrid search, metadata filtering, and index tuning, implementation of LLM evaluation, governance, tracing, and monitoring tools, familiarity with CI/CD pipelines, Infrastructure-as-Code, and cloud-native deployment practices, prior work experience in the Oil and Gas industry, experience with Dataiku DSS, knowledge of SRE practices.

• No conditions specified