Senior Python Engineer – MCP Connector Architecture

Alpharetta, GA - USA

Monthly Salary: Not Disclosed

Posted on: 4 hours ago

Vacancies: 1 Vacancy

Job Summary

Senior Python Engineer MCP Connector Architecture

Our enterprise client is assembling a specialized team to architect and build Model Context Protocol (MCP) connectors-the critical infrastructure enabling AI agents to interact with enterprise data platforms at scale. This is foundational work: youll be designing the connector layer that powers the next generation of agentic AI systems.

Responsibilities

Architect and implement MCP connectors for enterprise data platforms (Databricks Snowflake as initial targets)
Build robust Python services and libraries that form the connector framework
Design authentication flows API integrations and data access patterns for AI agent consumption
Shape early-stage architecture decisions while maintaining production code quality
Establish testing strategies CI/CD pipelines and deployment infrastructure
Collaborate directly with AI/ML teams on connector behavior agent requirements and platform evolution

Qualifications

Deep Python expertise (3 5 years building production systems not just scripting)
Hands-on MCP experience (1 year working directly with Model Context Protocol)
Production engineering mindset: youve shipped Python applications that run at scale
Strong MCP fundamentals: understanding of protocol semantics lifecycle management and agent interaction patterns
FastMCP library experience (or similar MCP tooling)
Independent execution: you own problems end-to-end and deliver without hand-holding
Experience with REST APIs OAuth/authentication flows and third-party integrations
Working knowledge of SQL (Postgres preferred) CI/CD and Git workflows

Required Skills

Machine Learning background: experience with model development training pipelines or ML infrastructure
LLM experience: hands-on work building fine-tuning or deploying large language models
GenAI development: proven track record building generative AI applications RAG systems or agent architectures
MCP expertise: direct experience with Model Context Protocol in production contexts

Preferred Skills

ML/GenAI depth: model training LLM fine-tuning prompt engineering embedding pipelines
Prior work building connectors/integrations for Databricks Snowflake or similar data platforms
Experience with RAG architectures vector databases or semantic search systems
Background in agentic AI systems LangChain/LlamaIndex or agent orchestration frameworks
Understanding of LLM observability evaluation frameworks or model serving infrastructure
Experience with large-scale integration platforms or multi-connector ecosystems

Senior Python Engineer MCP Connector Architecture Our enterprise client is assembling a specialized team to architect and build Model Context Protocol (MCP) connectors-the critical infrastructure enabling AI agents to interact with enterprise data platforms at scale. This is foundational work: y...

Senior Python Engineer MCP Connector Architecture

Responsibilities

Architect and implement MCP connectors for enterprise data platforms (Databricks Snowflake as initial targets)
Build robust Python services and libraries that form the connector framework
Design authentication flows API integrations and data access patterns for AI agent consumption
Shape early-stage architecture decisions while maintaining production code quality
Establish testing strategies CI/CD pipelines and deployment infrastructure
Collaborate directly with AI/ML teams on connector behavior agent requirements and platform evolution

Qualifications

Deep Python expertise (3 5 years building production systems not just scripting)
Hands-on MCP experience (1 year working directly with Model Context Protocol)
Production engineering mindset: youve shipped Python applications that run at scale
Strong MCP fundamentals: understanding of protocol semantics lifecycle management and agent interaction patterns
FastMCP library experience (or similar MCP tooling)
Independent execution: you own problems end-to-end and deliver without hand-holding
Experience with REST APIs OAuth/authentication flows and third-party integrations
Working knowledge of SQL (Postgres preferred) CI/CD and Git workflows

Required Skills

Machine Learning background: experience with model development training pipelines or ML infrastructure
LLM experience: hands-on work building fine-tuning or deploying large language models
GenAI development: proven track record building generative AI applications RAG systems or agent architectures
MCP expertise: direct experience with Model Context Protocol in production contexts

Preferred Skills

ML/GenAI depth: model training LLM fine-tuning prompt engineering embedding pipelines
Prior work building connectors/integrations for Databricks Snowflake or similar data platforms
Experience with RAG architectures vector databases or semantic search systems
Background in agentic AI systems LangChain/LlamaIndex or agent orchestration frameworks
Understanding of LLM observability evaluation frameworks or model serving infrastructure
Experience with large-scale integration platforms or multi-connector ecosystems