GenAI Engineer

Job Role: GenAI Engineer
Experience: 6+ Years
Location: New Delhi
Notice Period: Immediate Joiner
Work Model: 5 Days Work From Office

About the Role

We are seeking a highly skilled GenAI Engineer to design, develop, and deploy enterprise-grade Generative AI solutions for real-world business applications. The ideal candidate will have hands-on experience delivering production-ready GenAI projects, with expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and AI workflow orchestration.

This role offers an opportunity to build innovative AI solutions from concept to deployment while working closely with business stakeholders, data teams, and engineering teams to create scalable and secure GenAI applications on Azure and Databricks.

Key Responsibilities

Generative AI Development

• Design, develop, and deploy production-grade Generative AI solutions for enterprise use cases.

• Build and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, and semantic search.

• Create advanced prompt engineering frameworks including system prompts, few-shot prompting, structured outputs, and agent workflows.

• Develop LLM orchestration workflows involving chaining, routing, memory management, and tool/function calling.

• Fine-tune or adapt LLMs to improve performance, accuracy, and business relevance.

• Deliver scalable GenAI solutions beyond proof-of-concepts, with demonstrated experience in production deployments.

End-to-End Solution Development

• Own GenAI initiatives from problem definition and architecture design through development, validation, deployment, and optimization.

• Convert business requirements into practical AI-powered solutions.

• Build reusable GenAI components, frameworks, and accelerators for future projects.

Azure & Cloud Integration

• Implement and deploy GenAI solutions using Microsoft Azure services.

• Work extensively with:

• Azure OpenAI

• Azure Web Apps

• Azure Function Apps

• Azure Virtual Machines

• Design secure, scalable, and highly available cloud-based AI applications.

• Collaborate with infrastructure and security teams to ensure compliance and governance.

Databricks & Data Integration

• Utilize Databricks for data preparation, experimentation, and AI model workflows.

• Integrate structured and unstructured enterprise data into RAG architectures.

• Work closely with Data Engineering teams to establish reliable data pipelines and retrieval systems.

API & Application Development

• Develop APIs and backend services using Python frameworks such as FastAPI or Flask.

• Integrate GenAI capabilities into enterprise applications and business workflows.

• Ensure performance, scalability, reliability, and maintainability of AI-powered services.

Testing, Monitoring & Governance

• Validate AI outputs for quality, safety, explainability, and accuracy.