AI Engineer

Build & Hire; Maharashtra; On-site; INR 1,500,000 - 2,000,000; Full time

Job summary

Build & Hire is seeking an experienced AI Engineer to design and deploy production-grade AI solutions using LLMs, Agentic AI, and MCP. The role emphasizes building AI-powered workflows interfacing with enterprise systems, APIs, and databases.

You'll implement end-to-end agent architectures, guardrails, and secure integrations in a hands-on environment. The ideal candidate will ship production-ready AI applications, implement robust backends, and work across Python, cloud-native tech, and vector

Qualifications

• 3–6 years of professional software development or AI engineering experience.
• Strong hands-on Python programming skills.
• Practical experience building LLM / Generative AI applications.
• Hands-on experience building or implementing Agentic AI / AI Agent solutions.
• Practical understanding and implementation experience with MCP.
• Strong understanding of tool calling, function calling, and structured outputs.
• Experience building REST APIs and microservices.
• Hands-on experience implementing RAG pipelines, embeddings, and vector databases.
• Strong understanding of prompt engineering and LLM orchestration.
• Experience integrating LLMs with external tools, APIs, databases, or enterprise systems.
• Experience with Git, Docker, and CI/CD.
• Strong debugging, problem-solving, and software engineering skills.
• Ability to build production-oriented solutions rather than only experimental AI prototypes.

Responsibilities

• Design and develop Agentic AI workflows using LLMs and autonomous or semi-autonomous AI agents.
• Build and integrate MCP Servers and MCP Clients to connect AI agents with enterprise applications, APIs, databases, and tools.
• Develop multi-agent workflows involving planning, reasoning, tool calling, memory, orchestration, and task execution.
• Build production-grade LLM applications using frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, or similar technologies.
• Integrate LLMs and foundation models from OpenAI, Azure OpenAI, Anthropic, Gemini, and open-source model providers.
• Develop robust backend services and APIs using Python, FastAPI, Flask, or Django.
• Design and implement RAG pipelines, embeddings, vector search, and enterprise knowledge retrieval systems.
• Integrate AI applications with REST APIs, databases, SaaS platforms, and enterprise systems.
• Implement tool and function calling with structured outputs and reliable execution workflows.
• Design AI agent guardrails and validation mechanisms to ensure safe and reliable execution.

Skills

Python

LLM development

Agentic AI

MCP

Tool calling

REST APIs

RAG pipelines

Prompt engineering

LLM integration

Git / Docker / CI-CD

Debugging

Production-ready

Full-stack development

Education

Bachelor's/Master's in CS/Engineering/AI

Tools

LangGraph

LangChain

LlamaIndex

Semantic Kernel

Job description

AI Engineer – Agentic AI & MCP

We are currently hiring for an experienced AI Engineer / Agentic AI Developer with 3–6 years of experience to design and build production-grade AI applications using Large Language Models, Agentic AI, and the Model Context Protocol (MCP).

The role focuses on building AI-powered solutions that can interact with enterprise applications, APIs, databases, business systems, and tools through intelligent agent workflows.

You will work across Python, LLMs, Agentic AI, MCP, RAG, AI orchestration, APIs, vector databases, and cloud-native technologies, while working closely with business and engineering teams to convert enterprise use cases into secure, scalable, production-ready AI solutions.

This role is suited to a hands-on AI builder rather than someone with only theoretical Generative AI knowledge. You should be comfortable taking an AI use case from an initial prototype through to a robust production implementation.

Key Responsibilities

• Design and develop Agentic AI workflows using LLMs and autonomous or semi-autonomous AI agents.
• Build and integrate MCP Servers and MCP Clients to connect AI agents with enterprise applications, APIs, databases, and tools.
• Develop multi-agent workflows involving planning, reasoning, tool calling, memory, orchestration, and task execution.
• Build production-grade LLM applications using frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, or similar technologies.
• Integrate LLMs and foundation models from OpenAI, Azure OpenAI, Anthropic, Gemini, and open-source model providers.
• Develop robust backend services and APIs using Python, FastAPI, Flask, or Django.
• Design and implement RAG pipelines, embeddings, vector search, and enterprise knowledge retrieval systems.
• Integrate AI applications with REST APIs, databases, SaaS platforms, and enterprise systems.
• Implement tool and function calling with structured outputs and reliable execution workflows.
• Design AI agent guardrails and validation mechanisms to ensure safe and reliable execution.
• Implement authentication and authorization mechanisms for AI applications and enterprise integrations.
• Build observability, tracing, evaluation, and monitoring capabilities for AI applications.
• Implement human-in-the-loop mechanisms where AI decisions or actions require validation or approval.
• Containerize and deploy AI applications using Docker, Kubernetes, and cloud platforms.
• Work with engineering and business teams to understand enterprise use cases and translate them into working AI solutions.
• Develop prototypes rapidly and subsequently harden successful solutions for production deployment.
• Debug, test, optimize, and continuously improve AI applications and agent workflows.

Required Skills / Primary Skills

• 3–6 years of professional software development or AI engineering experience.
• Strong hands-on Python programming skills.
• Practical experience building LLM / Generative AI applications.
• Hands-on experience building or implementing Agentic AI / AI Agent solutions.
• Practical understanding and implementation experience with Model Context Protocol (MCP).
• Strong understanding of tool calling, function calling, and structured outputs.
• Experience building REST APIs and microservices.
• Hands-on experience implementing RAG pipelines, embeddings, and vector databases.
• Strong understanding of prompt engineering and LLM orchestration.
• Experience integrating LLMs with external tools, APIs, databases, or enterprise systems.
• Experience with Git, Docker, and CI/CD.
• Strong debugging, problem-solving, and software engineering skills.
• Ability to build production-oriented solutions rather than only experimental AI prototypes.

Additional Skills

• Full-stack development experience with React, Next.js, or Node.js.
• Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, or Semantic Kernel.
• Experience working with Azure, AWS, or GCP.
• Experience with PostgreSQL, MongoDB, or Redis.
• Experience with vector databases such as Pinecone, Qdrant, Weaviate, Milvus, or pgvector.
• Experience integrating AI applications with enterprise platforms such as SAP, Salesforce, PLM/Teamcenter, or other business systems.
• Experience implementing AI security, evaluation, tracing, observability, and monitoring.
• Experience designing AI guardrails and validation frameworks.
• Experience with human-in-the-loop AI workflows.
• Understanding of AI agent memory, planning, reasoning, and orchestration patterns.
• Experience with cloud-native application deployment and Kubernetes.
• Experience developing enterprise-grade APIs and integration services.
• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related discipline.

AI Engineering & Architecture

The ideal candidate should understand how to connect LLMs to real-world enterprise systems rather than treating an LLM as a chatbot sitting in a browser.

A Typical Solution May Involve a Workflow Such As

Enterprise System → MCP Server → AI Agent → LLM → Tool/API Execution → Validation → User/Application

You should be comfortable designing, implementing, debugging, and productionizing components across this workflow.

The candidate should also understand how to build systems involving:

• AI agents and multi-agent workflows.
• Planning and reasoning.
• Tool and function calling.
• MCP Servers and MCP Clients.
• LLM orchestration.
• Enterprise knowledge retrieval.
• RAG and vector search.
• Authentication and authorization.
• Guardrails and validation.
• Human-in-the-loop workflows.
• AI evaluation and observability.
• Secure enterprise system integration.

Employment Details

• Location: Pune
• Industry: Artificial Intelligence / Enterprise Technology
• Role: AI Engineer / Agentic AI Developer
• Experience: 3–6 years
• Employment Type: Full Time
• Compensation: 15-20 Lakhs DOE

If you're a hands-on AI Engineer who has actually built LLM applications, Agentic AI workflows, and enterprise integrations, this is an opportunity to work on practical AI systems that move beyond simple chat interfaces.

You will work at the intersection of Generative AI, AI Agents, MCP, enterprise software, APIs, and cloud-native engineering, helping turn real business use cases into secure and production-ready AI solutions.