Senior Generative AI Engineer

Role Overview :

We are looking for an experienced Senior Gen AI Engineer to design, develop, and deploy next-generation Artificial Intelligence solutions. The ideal candidate will have strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Python, Data Engineering, and MLOps.

The candidate will be responsible for building scalable AI applications, productionizing ML/AI pipelines, developing intelligent automation solutions, and collaborating with engineering and business teams to deliver enterprise-grade AI capabilities.

Key Responsibilities :1. Generative AI Solution Development :

• Design and develop enterprise-level Generative AI applications using LLM-based architectures.

• Build and optimize RAG pipelines for knowledge retrieval, question answering, and AI-powered solutions.

• Develop AI Agent workflows using agent orchestration frameworks.

• Integrate LLMs with enterprise systems, APIs, and data platforms.

• Implement prompt engineering techniques to improve AI response quality and accuracy.

2. AI Application Engineering :

• Develop scalable backend services and AI applications using Python.

• Build APIs and services to support AI-powered applications.

• Design solutions for LLM integration, data processing, and AI automation.

• Work on AI application architecture, performance optimization, and scalability.

3. Data Engineering & AI Pipelines :

• Design and develop data pipelines required for AI/ML workloads.

• Perform data extraction, transformation, validation, and preparation.

• Develop optimized SQL queries and work with structured and unstructured data.

• Implement data workflows supporting RAG and AI applications.

4. MLOps & Production Deployment :

• Deploy and maintain AI/ML solutions in production environments.

• Build CI/CD pipelines for AI application deployment.

• Implement model monitoring, versioning, and performance tracking.

• Work with containerization and orchestration technologies.

5. AI Research & Innovation :

• Evaluate emerging GenAI technologies and frameworks.

• Research improvements in LLM performance, RAG architecture, and AI Agent capabilities.

• Recommend AI-driven solutions for business problems.

• Contribute to AI best practices and knowledge-sharing initiatives.