AI Platform Engineer and Architect

Kolkata
July 27, 2026
Full Time
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Job Description

An AI Platform Engineer is responsible for designing, developing, and operating the core AI platform that powers agentic AI systems. The role focuses on building scalable agent orchestration platforms, Model Context Protocol (MCP) integrations, CI/CD pipelines for AI agents, LLM integrations, RAG pipelines, evaluation frameworks, and platform governance. The engineer also researches emerging agentic AI frameworks and ensures reliable, secure, and high-performance AI platform operations.

Responsibilities

AI Platform Development

  • Design, build, and maintain the core AI platform for agentic systems.
  • Develop agent orchestration layers and platform services.
  • Build and manage Agent Store registries.
  • Design scalable platform architecture for AI applications.
  • Create reusable platform components and integration templates.

Agentic AI Development

  • Build applications using LangGraph, CrewAI, AutoGen, or similar frameworks.
  • Design and manage multi-agent workflows.
  • Develop AI agent orchestration pipelines.
  • Research and evaluate emerging agentic AI frameworks.
  • Recommend framework adoption based on technical evaluation.

MCP Integration

  • Design and implement Model Context Protocol (MCP) servers.
  • Integrate MCP services with AI agents.
  • Develop secure communication between AI agents and enterprise systems.
  • Maintain MCP platform integrations.

LLM Integration

  • Integrate OpenAI, Azure OpenAI, and Anthropic APIs.
  • Develop prompt engineering workflows.
  • Implement prompt evaluation strategies.
  • Build AI guardrails and safety mechanisms.
  • Optimize LLM performance and reliability.

RAG & Knowledge Systems

  • Design Retrieval-Augmented Generation (RAG) pipelines.
  • Build vector database integrations.
  • Manage embeddings and semantic search.
  • Optimize knowledge retrieval performance.
  • Maintain vector store infrastructure.

CI/CD & DevOps

  • Build CI/CD pipelines for AI agents.
  • Automate deployment, versioning, and rollback.
  • Containerize applications using Docker.
  • Deploy AI workloads on Kubernetes.
  • Maintain production deployment workflows.

AI Evaluation & Monitoring

  • Develop agent evaluation frameworks.
  • Measure output quality and model performance.
  • Implement drift detection mechanisms.
  • Monitor platform health and reliability.
  • Enforce AI guardrails and compliance policies.

Platform Operations

  • Maintain platform SLAs.
  • Troubleshoot platform incidents.
  • Resolve production issues within SLA.
  • Monitor system availability and performance.
  • Improve platform scalability and reliability.

Enterprise Integration

  • Integrate AI platforms with Jira, SharePoint, Microsoft Dynamics 365 (D365), and SDLC tools.
  • Build enterprise automation workflows.
  • Support business application integrations.

Documentation & Governance

  • Create platform architecture documentation.
  • Develop integration templates.
  • Document best practices and deployment procedures.
  • Maintain technical documentation for releases.

Required Skills

AI & Agentic AI

  • Agentic AI
  • LangGraph
  • CrewAI
  • AutoGen
  • Multi-Agent Systems
  • AI Agent Orchestration

Large Language Models

  • OpenAI API
  • Azure OpenAI
  • Anthropic Claude
  • Prompt Engineering
  • Prompt Evaluation
  • AI Guardrails

RAG & Knowledge Retrieval

  • Retrieval-Augmented Generation (RAG)
  • Vector Databases
  • Embeddings
  • Semantic Search
  • Knowledge Retrieval

MCP

  • Model Context Protocol (MCP)
  • MCP Server Development
  • MCP Integration

Programming

  • Python (Advanced)

Cloud & DevOps

  • Docker
  • Kubernetes
  • CI/CD
  • MLflow
  • Weights & Biases (W&B)

AI Operations

  • Agent Evaluation
  • Drift Detection
  • Model Monitoring
  • Performance Monitoring
  • Platform Reliability

Enterprise Tools

  • Jira
  • SharePoint
  • Microsoft Dynamics 365 (D365)
  • SDLC Integration

Architecture

  • Platform Architecture
  • System Design
  • API Integration
  • Distributed Systems

Soft Skills

  • Problem-solving
  • Analytical thinking
  • Research mindset
  • Communication
  • Collaboration
  • Technical documentation
  • Innovation
  • Platform ownership
  • Attention to detail
  • Continuous learning

Keywords

AI Platform Engineer, Agentic AI, LangGraph, CrewAI, AutoGen, MCP, Model Context Protocol, RAG, LLM, OpenAI, Azure OpenAI, Anthropic, Python, Docker, Kubernetes, CI/CD, MLflow, Weights & Biases, Prompt Engineering, Prompt Evaluation, AI Guardrails, Vector Database, Agent Orchestration, Platform Architecture, System Design, API Integration, SDLC, Jira, SharePoint, Microsoft Dynamics 365, Drift Detection, Model Monitoring, AI Platform, Multi-Agent Systems.

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