Hiring Generative AI Engineer in Delhi

We are a leading AI-driven product company building next-generation enterprise solutions powered by large language models and agentic architectures. Our team operates at the intersection of research and production, deploying scalable generative AI systems that solve complex business problems across industries. We are seeking a Generative AI Engineer to design, develop, and operationalize cutting-edge LLM applications from prototype to production.

In this role, you will architect end-to-end RAG pipelines, implement multi-agent frameworks using LangGraph and LangChain, and fine-tune open-source models with LoRA/QLoRA for domain-specific tasks. You will own vector search optimization with FAISS, build robust APIs with FastAPI, and deploy containerized workloads on AWS with CI/CD pipelines. Collaboration with product and data teams to define evaluation metrics, monitor model drift, and iterate rapidly is core to our workflow.

Our engineering culture emphasizes technical excellence, knowledge sharing, and pragmatic innovation. You will work alongside researchers and ML engineers in a flat hierarchy that values autonomous decision-making and continuous learning. We provide dedicated GPU compute budgets, conference access, and time for open-source contributions.

Requirements include 3+ years of hands-on experience with LLMs, proficiency in Python and modern MLOps tooling, and a track record of shipping generative AI features to production. Deep understanding of transformer architectures, prompt engineering strategies, and retrieval optimization is essential. Familiarity with MCP, agentic design patterns, and AWS services (SageMaker, Bedrock, Lambda) is highly valued.

We offer competitive compensation, equity, comprehensive health coverage, flexible hybrid work across Delhi NCR, and a clear progression path to Staff Engineer or AI Architect roles. The hiring process involves a technical screen, a system design discussion, and a take-home challenge reflecting real production scenarios. Final decisions within two weeks of onsite. Apply with your GitHub portfolio and a brief note on your most impactful LLM deployment. We are a leading AI-driven product company building next-generation enterprise solutions powered by large language models and agentic architectures. Our team operates at the intersection of research and production, deploying scalable generative AI systems that solve complex business problems across industries. We are seeking a Generative AI Engineer to design, develop, and operationalize cutting-edge LLM applications from prototype to production.

In this role, you will architect end-to-end RAG pipelines, implement multi-agent frameworks using LangGraph and LangChain, and fine-tune open-source models with LoRA/QLoRA for domain-specific tasks. You will own vector search optimization with FAISS, build robust APIs with FastAPI, and deploy containerized workloads on AWS with CI/CD pipelines. Collaboration with product and data teams to define evaluation metrics, monitor model drift, and iterate rapidly is core to our workflow.

Our engineering culture emphasizes technical excellence, knowledge sharing, and pragmatic innovation. You will work alongside researchers and ML engineers in a flat hierarchy that values autonomous decision-making and continuous learning. We provide dedicated GPU compute budgets, conference access, and time for open-source contributions.

Requirements include 3+ years of hands-on experience with LLMs, proficiency in Python and modern MLOps tooling, and a track record of shipping generative AI features to production. Deep understanding of transformer architectures, prompt engineering strategies, and retrieval optimization is essential. Familiarity with MCP, agentic design patterns, and AWS services (SageMaker, Bedrock, Lambda) is highly valued.

We offer competitive compensation, equity, comprehensive health coverage, flexible hybrid work across Delhi NCR, and a clear progression path to Staff Engineer or AI Architect roles. The hiring process involves a technical screen, a system design discussion, and a take-home challenge reflecting real production scenarios. Final decisions within two weeks of onsite. Apply with your GitHub portfolio and a brief note on your most impactful LLM deployment.