Lead AI Engineer

Location: Sector 63, Noida

Experience: 12+ Years

Employment Type: Full-Time

Work Mode: Work From Office

Team: Engineering / Technology

YES MADAM is looking for an experienced AI Lead to lead the architecture, development, and adoption of AI/GenAI solutions across the organization.

The ideal candidate should combine strong software architecture and backend engineering expertise in Java and Python with hands-on experience in Generative AI, LLMs, RAG, AI Agents, and scalable AI systems.

The role involves working closely with Engineering, Product, Data, and Technology leadership to identify high-impact AI opportunities and convert them into secure, scalable, reliable, production-ready solutions.

• Own the organizations AI/GenAI technology strategy and architecture.
• Design and lead scalable AI-powered applications and platforms.
• Build production-grade solutions using Python and Java.
• Lead development of LLM applications, RAG pipelines, AI Agents, and intelligent automation.
• Evaluate and integrate LLM platforms such as OpenAI, Google Gemini, AWS Bedrock, and other relevant models.
• Design architectures involving Vector Databases, Embeddings, Semantic Search, and RAG.
• Drive decisions around AI frameworks, model integration, orchestration, evaluation, and deployment.
• Integrate AI capabilities with existing Java/Spring Boot microservices and backend platforms.
• Define engineering best practices for prompt engineering, model evaluation, security, testing, observability, and responsible AI.
• Lead technical POCs and convert successful experiments into production-ready solutions.
• Identify and prioritize high-impact AI use cases with Product, Engineering, Data, and Business teams.
• Establish standards for code quality, scalability, reliability, security, performance, and observability.
• Mentor and guide AI/ML and backend engineering teams.
• Stay updated with GenAI, LLMs, Agentic AI, AI infrastructure, and developer tooling.
• Own the technical roadmap and contribute to broader technology strategy and architecture decisions.

Must Have:

• Strong hands-on experience with Java and Python
• Strong expertise in:
• Java
• Spring Boot
• REST APIs
• Microservices
• Strong understanding of:
• OOP
• Distributed Systems
• Scalable Backend Architecture
• System Design
• Experience building high-performance, production-grade applications

Must Have:

• Hands-on experience with Generative AI
• Experience building LLM-based applications
• Strong understanding of:
• LLMs
• RAG
• Prompt Engineering
• LLM Orchestration
• Experience building AI Agents / Agentic AI systems
• Experience integrating LLM APIs and models
• Experience with:
• Embeddings
• Semantic Search
• Vector Databases
• Understanding of AI application evaluation, monitoring, and optimization
• Experience taking AI solutions from POC Production

Experience with one or more:

• OpenAI / GPT APIs
• Google Gemini
• AWS Bedrock
• LangChain
• LangGraph
• LangChain4j
• Spring AI
• Pinecone
• Qdrant
• Milvus
• Weaviate
• AI/ML APIs
• Model-serving platforms

The candidate should have strong expertise in:

• System Design
• Solution Architecture
• Distributed Systems
• Scalable Architecture
• Technical Leadership
• Architecture Ownership
• Technology Roadmaps
• Engineering Strategy
• Cross-functional Team Leadership
• Stakeholder Management
• Mentoring
• Business-to-Technology Translation
• Problem Solving & Decision Making

• 12+ years overall software engineering experience
• Strong professional experience with both Java and Python
• Strong hands-on AI/GenAI experience
• Experience with LLMs, RAG, and AI Agents
• Experience designing AI application architecture
• Proven experience taking AI solutions from POC to Production
• Strong Java + Spring Boot + Microservices background
• Strong System Design / Solution Architecture expertise
• Experience with Cloud and Distributed Systems
• Technical leadership and architecture ownership
• Strong communication and stakeholder-management skills

• High-scale / consumer-facing technology experience
• Product company / startup experience
• Experience building AI products used by large user bases
• Strong AWS experience
• MLOps / LLMOps exposure
• Kubernetes
• Docker
• CI/CD
• Observability
• AI evaluation frameworks
• Model monitoring

Exposure to:

• Recommendation Systems
• Personalization
• Conversational AI
• Intelligent Automation

The AI Lead will be measured on:

1. Successful delivery of production-ready AI/GenAI solutions
2. Adoption of AI capabilities across business and technology platforms
3. Improvement in engineering productivity through AI
4. Scalable, secure, and reliable AI architecture
5. Successful conversion of POCs into production
6. Development and growth of the AI engineering team
7. Measurable business impact from AI initiatives