Job Title: Lead AI Engineer – Enterprise AI Platforms

Experience: 8–10 Years

Location: Hyderabad

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. The role requires strong ability to translate requirements into production implementations, make pragmatic engineering decisions, resolve technical blockers, and maintain quality throughout the delivery lifecycle.

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.
- Implement prompt lifecycle management including versioning, testing, release controls, rollback, and regression validation.
- Establish automated AI evaluation and regression pipelines covering response quality, grounding, hallucination, consistency, safety, latency, token consumption, and cost.

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.
- Lead bounded technical spikes to validate AI technology choices, performance, feasibility, and integration approaches; ensure experiments are time-boxed and closed with documented decisions and recommendations.

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.
- Robust 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 LLMs.
- Experience with AI governance, security, compliance, and enterprise AI controls.
- Experience building reusable AI platforms, accelerators, and developer tooling.

Required Qualifications

- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
- 8–10 years of software engineering experience with significant hands-on AI/ML and Generative AI development experience.
- Proven experience leading technical teams, mentoring engineers, and delivering enterprise-scale AI solutions.
- Strong analytical, communication, stakeholder management, and problem

Pay: Up to ₹2,500,000.00 per year

Work Location: In person

📌 Lead AI Engineer – Enterprise AI Platforms (Hyderabad)
🏢 TekFrameworks Consulting Private
📍 Hyderabad