platform engineer for AI agents

EPAM provides enterprise software products, open source solutions, and accelerators. Its Enterprise Agent Development Platform offers a cloud-native environment for defining, orchestrating, and observing AI agents at scale.

• Design and implement semantic discovery mechanisms for MCP servers using vector embeddings;
• Integrate Vault (HashiCorp) and AWS Secrets Manager for secure credential storage and retrieval;
• Develop and maintain Python APIs for credential lifecycle management and retrieval in runtime environments;
• Implement domain and capability taxonomy for MCP resource classification;
• Build indexing pipelines using OpenSearch or pgvector for semantic search and discovery;
• Collaborate on integrating semantic search with MCP orchestration workflows and developer tooling;
• Ensure secure handling of keys, tokens, and secrets in line with enterprise compliance standards;
• Optimize the performance and reliability of credential management components for large-scale deployments;
• Work closely with platform governance teams to embed access control and compliance into credential workflows.

• 3+ Years of experience in platform or backend engineering roles;
• Hands-on experience with secrets management platforms such as Vault (HashiCorp) or AWS Secrets Manager;
• Practical expertise implementing semantic or vector search with OpenSearch, pgvector, or an equivalent;
• Strong Python skills for backend API design and integration work;
• Knowledge of security, identity, and access management best practices for distributed systems;
• Nice to have: experience using AWS Bedrock embedding models for semantic indexing, familiarity with OpenSearch Service or pgvector on RDS for search infrastructure, understanding of OAuth token lifecycle management and associated security patterns, background in building enterprise-scale registry or resource management platforms.

• Fully remote working mode in Portugal.