Lead Python Engineer: MCP Protocol & Tool Integrations

EPAM Systems; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

EPAM Systems seeks a Lead Python Engineer to build MCP protocol servers and agent tool integrations within an Enterprise Agent Development Platform on AWS. You will design and implement scalable, cloud-native components that enable AI agents to be defined, orchestrated, deployed, and observed at scale.

Responsibilities include building MCP servers/clients with async Python, enabling tool integrations, translating APIs to MCP interfaces, and ensuring robust security and governance.

Qualifications

• 5+ years of Python engineering experience with agent/AI systems.
• Hands-on experience implementing MCP servers or clients in production.
• Portfolio of tool integrations for LLM agents (OpenAI functions, LangChain tools, etc.).
• Experience building async HTTP services and streaming protocols.

Responsibilities

• Build MCP protocol servers supporting Streamable HTTP transport, tools/list, tools/call, and capability negotiation.
• Create MCP server/client components using Python async programming and streaming responses.
• Develop agent tool integrations with function calling, tool schema definitions, and parameter validation.
• Set up MCP target types, including Lambda functions as tools and remote MCP servers.
• Handle tool metadata management: schema registration, tagging, and versioning.
• Register targets in AWS AgentCore Gateway for tool discovery.
• Convert existing APIs into MCP-compliant interfaces.
• Collaborate with teams to standardize tool integration patterns.

Skills

Python

Async programming

Agent/AI systems

AWS Lambda

MCP protocol

OpenAPI to MCP

LangGraph / LangChain

Tools

OpenAI functions

LangGraph tools

AWS Gateway

Job description

EPAM Systems seeks a Lead Python Engineer to build MCP protocol servers and agent tool integrations within an Enterprise Agent Development Platform on AWS. You will design and implement scalable, cloud-native components that enable AI agents to be defined, orchestrated, deployed, and observed at scale.

Responsibilities include building MCP servers/clients with async Python, enabling tool integrations, translating APIs to MCP interfaces, and ensuring robust security and governance.