AI Lead Engineer

Role & responsibilities

KEY RESPONSIBILITIES

Architect and deliver scalable Gen AI, LLM, RAG, CAG & Agentic AI solutions for healthcare use cases

Lead full AI/ML lifecycle: problem framing, data engineering, model development, MLOps & LLMOps

Design and implement multi-Agent AI systems, Conversational AI, Chatbots & IVR platforms

Drive Prompt Engineering, Context Engineering, LLM Fine-tuning, and LLM Validation strategies

Integrate AI solutions via APIs with RDBMS, Vector DBs, Graph DBs, and AWS AI/ML services

Build and maintain CI/CD pipelines, model monitoring, deployment, and performance optimization

Collaborate with stakeholders for requirement gathering, business analysis, and solution design

Lead sprint planning, Agile delivery, risk management, and cross-functional collaboration

Mentor junior engineers, conduct code reviews, and drive innovation & continuous improvement

QUALIFICATIONS

B.Tech / M.Tech / M.S. / PhD in CS, AI, Data Science, or related field

5-8 years of hands-on experience in AI/ML, Data Science & Generative AI

Proven track record delivering production AI systems at enterprise scale

Strong foundation in Mathematics, Statistics, and algorithmic problem-solving

Experience in healthcare domain is a strong plus

MANAGEMENT & LEADERSHIP

Team Management, People Management, Hands-on AI Engineering, Solution Architecture, AI Strategy, AI/ML Solution Design, Team Mentoring & Coaching, Stakeholder Management, Customer Engagement, Requirement Gathering, Business Analysis, Agile & Scrum Delivery, Sprint Planning, Project Planning & Estimation, Risk & Dependency Management, Cross-functional Collaboration, Resource Planning, Innovation & Continuous Improvement.

Preferred candidate profile

Python, SQL, Mathematics, Statistics, Data Science, ML, Deep Learning, NLP, Gen AI, LLM, Prompt Engineering, RAG, CAG, Context Engineering, AI Agents, Agentic AI, MCP, AI Architecture, AI Frameworks, MLOps, LLMOps, Multi Model LLMS, Multi Agentic AI, Conversational AI, Chat Bot, IVR, LLM Validation, LLM as Judge, LLM Verifier, Agentic AI Frame works, API Integration, RDBMS, Postgre SQL, Vector DB, Graph DB, AWS ML , AWS ML/AI Services, Git, CI/CD, Model Monitoring, Model Deployment, Performance Optimization.

SECONDARY SKILLS

Data Preprocessing, Feature Engineering, Data Wrangling, LLM Fine-tuning, SLM, Embedding Models, Knowledge Graphs, Azure AI Services, AI Governance, Responsible AI, AI Security, Documentation, UAT, Production Support, Data Visualization, Docker, Kubernetes, Cloud AI Platforms (AWS/Azure/GCP), Automation Frameworks, Experiment Tracking, Monitoring & Observability.