Software Engineer – Generative AI

This role focuses on the end-to-end design, development, and deployment of generative AI applications. You will work at the intersection of full-stack development and machine learning, building responsive interfaces and robust backend services that leverage LLMs and agentic workflows. The position is based in Mumbai and operates on a hybrid work model.

Our client is a leading global provider of professional services and digital transformation solutions, specializing in advanced software engineering and data-driven innovation. They operate at a significant scale, partnering with major enterprises to integrate cutting-edge technologies into complex business environments. The company fosters a culture of continuous learning and technical excellence, empowering teams to solve high-impact challenges.

Responsibilities

• Design, develop, and maintain end-to-end generative AI applications and services.
• Build responsive, high-performance front-end interfaces using React.
• Develop backend services, APIs, and data pipelines using Python frameworks like FastAPI and Flask.
• Integrate and fine-tune large language models to meet specific business requirements.
• Design, test, and iterate on production-grade prompts and maintain them under version control.
• Implement retrieval-augmented generation (RAG) architectures and complex agentic workflows.
• Evaluate model performance, manage vector databases, and optimize inference pipelines.
• Ensure high code quality through rigorous testing, code reviews, and CI/CD practices.

Requirements

• 3 to 5 years of hands-on professional software development experience.
• Strong proficiency in Python and modern web development with React.
• Deep understanding of Generative AI concepts, including LLMs, prompt engineering, and RAG.
• Experience with vector databases such as Pinecone, Weaviate, or ChromaDB.
• Familiarity with cloud platforms like AWS, GCP, or Azure.
• Knowledge of AI orchestration frameworks such as LangGraph, CrewAI, or AutoGen.
• Experience with MLOps practices, Docker, and Kubernetes.
• Strong grasp of software engineering best practices including version control and testing.