Senior AI Engineer - ( MCP & LLM Integration)

This is a remote position. Senior AI Engineer - ( MCP & LLM Integration) About the Role We are looking for a high-performing Senior AI Engineer (including MCP) to lead the development and delivery of enterprise Model Context Protocol (MCP) integrations in Phase 1, and governed multi-model access, evaluation, and observability in Phase 2.

The ideal candidate will combine strong backend software engineering in Python with direct hands-on experience implementing the Model Context Protocol (MCP), LLM APIs (Anthropic Claude, OpenAI), tool-calling, and evaluation frameworks, ensuring robust, production-grade agentic workflows and reliable model interfaces across the engagement.

Programme Context & Phased Effort Allocation The engagement is structured as a 22-week sequential programme divided into two delivery phases, assuming a five-day working week:

Phase 1: MCP Gateway and Governance (Weeks 1–10 / 10 Weeks): Full-time allocation (1.0 FTE) developing reference MCP servers, API adapters, central registry onboarding checks, and validating Claude client tool-use workflows.

Phase 2

Model Access through Microsoft Foundry (Weeks 11–22 / 12 Weeks): Full-time allocation (1.0 FTE) integrating Claude, OpenAI, and specialist models, implementing streaming token protocols, usage attribution, and model evaluation harnesses.

Key Responsibilities

Reference MCP Servers & Adapters: Develop reference MCP servers and API adapters, including JSON-schema tool definitions, granular permissions, input validation, and graceful error handling.

Registry Integration & Onboarding

Implement MCP registry integration, automated onboarding checks, request/response logging, and telemetry ingestion.

Client Tool Connectivity

Connect the selected Claude client to approved MCP tools and thoroughly validate representative agentic workflows and tool-calling execution.

Multi-Model Provider Integration

Integrate Claude, OpenAI, and agreed specialist model endpoints through the approved Azure APIM gateway and Microsoft Foundry.

Streaming, Attribution & Evaluation

Implement and test provider interfaces, streaming token responses, tool use, usage attribution, prompt caching, and model evaluation suites.

Automated Testing & Documentation

Automate integration tests, resolve defects, and document reusable integration patterns and standard operating procedures (SOPs).

Required Skills & Experience

6–10+ years of software engineering experience with senior proficiency in Python (FastAPI, Pydantic, asyncio) or agreed backend integration languages.

Model Context Protocol (MCP) & Agentic Workflows

Hands-on experience implementing the Model Context Protocol (MCP) specification, JSON-RPC, tool-calling schemas, and client-server integrations.

Enterprise LLM Integration

Extensive experience integrating Anthropic Claude and OpenAI APIs, handling authentication, token rate limits, prompt caching, and streaming protocols.

Model Evaluation & Observability

Experience with automated model evaluation, benchmark datasets, prompt engineering, tracing (LangSmith / OpenTelemetry), and cost/usage attribution.

API Design & Automated Testing

Strong background in RESTful APIs, JSON schema validation, automated testing (pytest), mock services, and CI/CD integration.

Security & Data Governance

Understanding of zero-trust API security, payload sanitization, credential handling, and enterprise data privacy.

Preferred Qualifications

Capital Markets / Financial AI Applications: Experience deploying AI applications, document processing, or market research copilots in financial institutions.

Microsoft Foundry & Azure AI

Experience with Microsoft Foundry, Azure OpenAI Service endpoints, and Azure APIM policies.

Agent Frameworks

Familiarity with LangChain, LangGraph, AutoGen, or Semantic Kernel frameworks.

Open Source Contributions

Active contributions to open-source AI tooling, MCP server repositories, or developer SDKs.

Key Competencies

Model Context Protocol (MCP) Architecture Advanced Python Engineering & Asyncio LLM API Integration (Anthropic Claude & OpenAI) Agentic Tool-Calling & Schema Validation Model Evaluation, Benchmarking & Prompt Optimization Automated Testing & Integration Test Suites Production Telemetry & Token Usage Attribution Developer Tooling & Pattern Documentation

Requirements Required Skills & Experience

6–10+ years of software engineering experience with senior proficiency in Python (FastAPI, Pydantic, asyncio) or agreed backend integration languages.

Model Context Protocol (MCP) & Agentic Workflows

Hands-on experience implementing the Model Context Protocol (MCP) specification, JSON-RPC, tool-calling schemas, and client-server integrations.

Enterprise LLM Integration

Extensive experience integrating Anthropic Claude and OpenAI APIs, handling authentication, token rate limits, prompt caching, and streaming protocols.

Model Evaluation & Observability

Experience with automated model evaluation, benchmark datasets, prompt engineering, tracing (LangSmith / OpenTelemetry), and cost/usage attribution.

API Design & Automated Testing

Strong background in RESTful APIs, JSON schema validation, automated testing (pytest), mock services, and CI/CD integration.

Security & Data Governance

Understanding of zero-trust API security, payload sanitization, credential handling, and enterprise data privacy.

Benefits

Key Competencies

Model Context Protocol (MCP) Architecture Advanced Python Engineering & Asyncio LLM API Integration (Anthropic Claude & OpenAI) Agentic Tool-Calling & Schema Validation Model Evaluation, Benchmarking & Prompt Optimization Automated Testing & Integration Test Suites Production Telemetry & Token Usage Attribution Developer Tooling & Pattern Documentation

Originally posted on Himalayas