AI Lead Engineer (m/f/d)

Job Description

Role Overview - AI Lead Engineer (GenAI & Agentic AI) Location - Pune / Hyderabad Experience: 10-15+ Years (including 3-5+ years in AI/ML, Generative AI, or Agentic AI Solutions)

Role Overview We are looking for an experienced AI Lead Engineer to drive the design, architecture, and delivery of enterprise-grade AI solutions leveraging Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. The ideal candidate will have a strong software engineering background combined with expertise in AI solution design, AI agent orchestration, cloud platforms, and modern MLOps practices.

Key Responsibilities

• Lead the design, architecture, and implementation of AI/GenAI solutions.

• Build and deploy scalable LLM-powered applications and RAG systems.

• Design and implement Agentic AI and Multi-Agent systems for business automation and intelligence.

• Define enterprise AI architecture, governance, security, and best practices.

• Collaborate with business stakeholders, product teams, and engineering teams to deliver AI solutions.

• Mentor development teams and drive AI innovation initiatives.

• Evaluate emerging AI technologies and recommend adoption strategies.

Technical Skills

• Strong expertise in AI Solution Architecture & Design.

• Strong programming skills in Python and Temporal.

• Experience with TensorFlow, PyTorch, Scikit-learn (preferred).

• Expertise in Generative AI, LLMs, RAG, Vector Databases, AI Agents, and Multi-Agent Systems.

• Hands-on experience with LangChain, LlamaIndex, Semantic Kernel, LangGraph, CrewAI, AutoGen, or similar frameworks.

• Strong understanding of Prompt Engineering, Fine-Tuning, Embeddings, and RAG Optimization.

• Experience with Azure OpenAI, OpenAI GPT Models, Claude, Gemini, Llama, Mistral, or equivalent foundation models.

• Knowledge of Model Context Protocol (MCP), Tool Calling, Function Calling, and Agent Orchestration.

• Experience with Pinecone, Qdrant, Weaviate, ChromaDB, FAISS, Azure AI Search, or similar vector databases.

• Experience with Azure AI Services, Azure OpenAI, AWS AI/ML Services, or Google Vertex AI.

• Strong understanding of MLOps/LLMOps practices using MLflow, Kubeflow, Databricks, Azure ML, etc.

• Experience with APIs, microservices, Docker, Kubernetes, and cloud-native architectures.

• Knowledge of SQL, NoSQL databases, and data engineering concepts.

• Experience with AI monitoring, observability, evaluation frameworks, and governance practices.

Preferred Qualifications

• Experience in GraphRAG, Knowledge Graphs, and Enterprise Search solutions.

• Exposure to AI governance, compliance, and Responsible AI practices.

• Experience leading AI transformation initiatives and customer-facing engagements.

Qualifications

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Your Task

We are looking for an experienced AI Lead Engineer to drive the design, architecture, and delivery of enterprise-grade AI solutions leveraging Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. The ideal candidate will have a strong software engineering background combined with expertise in AI solution design, AI agent orchestration, cloud platforms, and modern MLOps practices. This role requires 10-15+ years of experience, including 3-5+ years in AI/ML, Generative AI, or Agentic AI Solutions. This position is based in Pune or Hyderabad.

Key Responsibilities

• Lead the design, architecture, and implementation of AI/GenAI solutions

• Build and deploy scalable LLM-powered applications and RAG systems

• Design and implement Agentic AI and Multi-Agent systems for business automation and intelligence

• Define enterprise AI architecture, governance, security, and best practices

• Collaborate with business stakeholders, product teams, and engineering teams to deliver AI solutions

• Mentor development teams and drive AI innovation initiatives

• Evaluate emerging AI technologies and recommend adoption strategies

Your Profile

We are looking for an experienced AI Lead Engineer with 10-15+ years of experience including 3-5+ years in AI/ML, Generative AI, or Agentic AI Solutions. The ideal candidate will have a strong software engineering background combined with expertise in AI solution design, AI agent orchestration, cloud platforms, and modern MLOps practices.

Must Have Skills

• 10-15+ years of professional experience

• 3-5+ years of experience in AI/ML, Generative AI, or Agentic AI Solutions

• Strong expertise in AI Solution Architecture and Design

• Strong programming skills in Python

• Strong programming skills in Temporal

• Expertise in Generative AI

• Expertise in Large Language Models (LLMs)

• Expertise in Retrieval-Augmented Generation (RAG)

• Expertise in Vector Databases

• Expertise in AI Agents and Multi-Agent Systems

• Hands-on experience with LangChain, LlamaIndex, Semantic Kernel, LangGraph, CrewAI, AutoGen, or similar frameworks

• Strong understanding of Prompt Engineering

• Strong understanding of Fine-Tuning

• Strong understanding of Embeddings

• Strong understanding of RAG Optimization

• Experience with Azure OpenAI, OpenAI GPT Models, Claude, Gemini, Llama, Mistral, or equivalent foundation models

• Knowledge of Model Context Protocol (MCP)

• Knowledge of Tool Calling

• Knowledge of Function Calling

• Knowledge of Agent Orchestration

• Experience with Pinecone, Qdrant, Weaviate, ChromaDB, FAISS, Azure AI Search, or similar vector databases

• Experience with Azure AI Services, Azure OpenAI, AWS AI/ML Services, or Google Vertex AI

• Strong understanding of MLOps/LLMOps practices using MLflow, Kubeflow, Databricks, Azure ML, etc.

• Experience with APIs and microservices

• Experience with Docker

• Experience with Kubernetes

• Experience with cloud-native architectures

• Knowledge of SQL databases

• Knowledge of NoSQL databases

• Knowledge of data engineering concepts

• Experience with AI monitoring, observability, evaluation frameworks, and governance practices

Nice to Have Skills

• Experience with TensorFlow

• Experience with PyTorch

• Experience with Scikit-learn

• Experience in GraphRAG

• Experience in Knowledge Graphs

• Experience in Enterprise Search solutions

• Exposure to AI governance

• Exposure to compliance practices

• Exposure to Responsible AI practices

• Experience leading AI transformation initiatives

• Experience with customer-facing engagements