Agentic AI / MCP Lead Engineer

Mployee.me; Mumbai; On-site; INR 14,299,000 - 21,926,000; Full time

Job summary

Agentic AI invites a MCP Lead Engineer to shape enterprise AI agent platforms across customer and internal workflows in a high-scale environment.

Location-based roles include Philadelphia and New York, USA, with a 6-month contract and work-from-office model. The role requires 6+ years software engineering and 3+ years in GenAI, with hands-on framework experience.

Qualifications

• 6+ years of software engineering experience with 3+ years in GenAI solutions.
• Hands-on experience building AI agents using frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, OpenAI Agents SDK.

Responsibilities

• Define the enterprise architecture for Agentic AI platforms and solutions.
• Design autonomous and semi-autonomous agent workflows for customer, employee, and operational use cases.
• Establish architectural patterns for multi-agent collaboration, orchestration, planning, reasoning, and execution.
• Drive technical vision for AI-powered business transformation initiatives.
• Design and implement MCP-based integrations between agents, tools, enterprise systems, and knowledge sources.
• Develop reusable MCP servers and connectors for business applications, APIs, databases, and enterprise platforms.
• Lead engineering teams building enterprise AI agent solutions.
• Mentor engineers on AI architecture, MCP, agent frameworks, and GenAI best practices.

Skills

Software engineering

GenAI

LangGraph

LangChain

Semantic Kernel

AutoGen

CrewAI

OpenAI Agents SDK

Tools

LangGraph

Job description

Job Summary

Agentic AI / MCP Lead Engineer

Location: Philadelphia, New York, USA

Experience Level: 6+ years.

Who Can Apply: US Citizen, Green Card Holder, Valid US Work permit

Key Responsibilities

Agentic AI Strategy Architecture

• Define the enterprise architecture for Agentic AI platforms and solutions.
• Design autonomous and semi-autonomous agent workflows for customer, employee, and operational use cases.
• Establish architectural patterns for multi-agent collaboration, orchestration, planning, reasoning, and execution.
• Drive technical vision for AI-powered business transformation initiatives.

MCP (Model Context Protocol) Enablement

• Design and implement MCP-based integrations between agents, tools, enterprise systems, and knowledge sources.
• Develop reusable MCP servers and connectors for business applications, APIs, databases, and enterprise platforms.
• Define standards for tool discovery, context management, permissions, and secure access controls.
• Ensure interoperability between AI models, agents, and enterprise ecosystems.

AI Agent Development

• Build intelligent agents capable of:
• Develop agent memory, context handling, and state management capabilities.
• Implement human-in-the-loop controls and agent governance mechanisms.

RAG Knowledge Systems

• Design and implement enterprise RAG solutions.
• Integrate structured and unstructured data sources into agent workflows.
• Optimize retrieval quality, grounding, relevance ranking, and response accuracy.

Platform Engineering Integration

• Architect integrations with:
• Enterprise APIs
• CRM, ERP, and payment systems
• Knowledge repositories
• Search platforms
• Cloud AI services
• Data platforms
• Build scalable backend services supporting agent operations.
• Develop reusable frameworks, SDKs, accelerators, and integration patterns.

Governance, Security Responsible AI

• Define guardrails for AI agents, tool usage, and autonomous actions.
• Implement authentication, authorization, auditing, and monitoring controls.
• Ensure compliance with enterprise security, privacy, and regulatory requirements.
• Establish observability, traceability, and evaluation frameworks.

Performance, Reliability Optimization

• Measure agent effectiveness, latency, cost efficiency, and task completion rates.
• Optimize prompts, workflows, retrieval pipelines, and tool interactions.
• Implement testing frameworks, evaluation benchmarks, and quality assurance processes.
• Improve scalability and resiliency of agent platforms.

Technical Leadership

• Lead engineering teams building enterprise AI agent solutions.
• Mentor engineers on AI architecture, MCP, agent frameworks, and GenAI best practices.
• Collaborate with Product Managers, Architects, Data Scientists, and Business Stakeholders.
• Drive technical governance and engineering standards.

Required Skills Experience

Agentic AI LLM Engineering

• 6+ years of software engineering experience with 3+ years in GenAI solutions.
• Hands-on experience building AI agents using frameworks such as:
• LangGraph
• LangChain
• Semantic Kernel
• AutoGen
• CrewAI
• OpenAI Agents SDK
• Strong understanding of agent planning, reasoning, memory, and orchestration patterns.

MCP Integration Development

• Experience implementing and extending Model Context Protocol (MCP).
• Building MCP servers, tool adapters, connectors, and integration layers.
• Deep understanding of tool calling, context sharing, and enterprise system integration.

Data Retrieval Technologies

• Vector databases (Pinecone, Weaviate, Azure AI Search, Elasticsearch, OpenSearch, ChromaDB)
• Embeddings, semantic search, hybrid search, and retrieval optimization
• Data pipelines and knowledge engineering

Preferred Qualifications

• Experience designing enterprise Copilot or Agent platforms.
• Exposure to Payments, Banking, Financial Services, or Digital Commerce domains.
• Knowledge of workflow orchestration tools such as Airflow, Temporal, or Prefect.
• Familiarity with GraphRAG, Knowledge Graphs, and advanced retrieval architectures.
• Experience with Azure AI Foundry, OpenAI, Anthropic, Gemini, or multi-model ecosystems.

Key Deliverables

• Enterprise Agentic AI architecture
• MCP integration framework and server ecosystem
• Multi-agent orchestration platform
• RAG and knowledge retrieval architecture
• Tool and connector catalog
• Security and governance framework
• Agent evaluation and monitoring framework
• Technical standards and engineering best practices
• Production-ready agentic use cases and accelerators
• AI platform roadmap with stakeholder approval

Contract Details

• Type: Contract
• Duration: 6 Month (Extendable)
• Working Model: WFO

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.