Lead AI/ML Engineer Enterprise AI Platforms

Job Summary:

We are seeking a highly skilled Lead AI/ML Engineer to lead the development, deployment, and optimization of enterprise-scale AI solutions. The ideal candidate will have strong hands-on experience in Generative AI, Agentic AI, Large Language Models (LLMs), RAG systems, AI orchestration frameworks, and AI platform engineering.

This is a hands-on technical leadership role responsible for delivering scalable AI solutions, mentoring engineers, and driving engineering excellence across enterprise AI initiatives.

Key Responsibilities

AI Solution Development & Engineering

• Design, develop, and deploy enterprise-grade AI applications and intelligent automation solutions.
• Build scalable AI systems leveraging Large Language Models (LLMs), AI agents, enterprise knowledge systems, and orchestration frameworks.
• Develop modular, reusable, and production-ready AI components that support enterprise scalability, reliability, and maintainability.
• Build AI-powered applications integrating models, tools, APIs, enterprise systems, and business workflows.

Agentic AI & Workflow Orchestration

• Develop Agentic AI solutions capable of reasoning, planning, tool usage, and workflow execution.
• Build and manage multi-agent systems using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LangFlow, N8N, or similar technologies.
• Implement MCP (Model Context Protocol), tool-calling frameworks, and AI workflow orchestration capabilities.
• Develop memory, context engineering, prompt orchestration, and agent evaluation mechanisms to improve AI effectiveness and reliability.

Enterprise Knowledge Systems & RAG

• Design and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines including ingestion, chunking, embeddings, vector indexing, retrieval, and grounded generation.
• Build Graph RAG and knowledge-centric AI systems using Neo4j and graph databases where appropriate.
• Develop enterprise search, semantic retrieval, and knowledge management capabilities.

Model Engineering & Evaluation

• Conduct model experimentation, evaluation, fine-tuning, and optimization activities across AI solutions.
• Develop AI evaluation frameworks covering response quality, hallucination detection, factual validation, policy compliance, and performance measurement.
• Optimize inference latency, throughput, token consumption, scalability, and operational costs.
• Integrate and manage multiple LLMs and SLMs to support diverse business use cases.

AI Platform Engineering & Operations

• Implement MLOps and LLMOps best practices including deployment, monitoring, versioning, governance, and lifecycle management.
• Deploy AI solutions on AWS, Azure, or GCP environments.
• Implement AI observability, monitoring, guardrails, security controls, and operational best practices.
• Build production-grade APIs, microservices, and AI services for enterprise consumption.

Technical Leadership

• Lead and mentor AI Engineers and Senior AI Engineers through technical guidance, design reviews, and code reviews.
• Drive engineering standards, reusable frameworks, development best practices, and delivery excellence.
• Collaborate closely with architects, product teams, engineering leaders, and business stakeholders to deliver high-impact AI solutions.
• Evaluate emerging AI technologies and recommend adoption where appropriate.

Required Skills

• Strong Python programming skills with experience in PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
• Hands-on experience building Generative AI and LLM-powered applications.
• Experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, LangFlow, or N8N.
• Experience with MCP, tool calling, workflow orchestration, and multi-agent systems.
• Strong experience in RAG, Graph RAG, vector databases, embeddings, semantic retrieval, and enterprise knowledge systems.
• Experience with Neo4j or similar graph databases.
• Experience in AI evaluation, model optimization, observability, and responsible AI practices.
• Strong understanding of distributed systems, scalable application design, and AI platform engineering.
• Experience with Docker, Kubernetes, MLflow, Airflow, CI/CD pipelines, and cloud-native deployments.
• Experience deploying AI applications on AWS, Azure, or GCP.
• Experience building production-grade APIs and microservices.

Preferred Skills

• Fine-tuning and adaptation of open-source LL…