Generative AI Engineer

As a Generative AI Engineer, you’ll be a core member of this pod, building and integrating agentic systems powered by cutting-edge LLM and GenAI technologies. You’ll work closely with Tech Leads and Full Stack Engineers to turn AI capabilities into production-ready enterprise solutions. Key Responsibilities • Design, develop, and deploy agentic AI systems leveraging LLMs and modern AI frameworks. • Integrate GenAI models into full-stack applications and internal workflows. • Collaborate on prompt engineering, model fine-tuning, and evaluation of generative outputs. • Build reusable components and services for multi-agent orchestration and task automation. • Optimize AI inference pipelines for scalability, latency, and cost efficiency. • Participate in architectural discussions, contributing to the pod’s technical roadmap. Core Skills & Experience Must Haves • 4–8 years of software engineering experience with at least 1–2 years in AI/ML or GenAI systems in production • Hands-on experience with Python only for AI/ML model integration. • Experience with LLM frameworks (LangChain, LlamaIndex is a must • Exposure to agentic frameworks (Langgraph, AutoGen, CrewAI is a must • Understanding of Git, CI/CD, DevOps, and production-grade GenAI deployment practices. Nice-to-Have • Familiarity with Google Cloud Platform (GCP) — especially Vertex AI, Cloud Run, and GKE. • Experience building AI APIs, embeddings, vector search, and integrating them into applications. • Experience fine-tuning open-source models (LLaMA, Mistral, etc.) or working with OpenAI APIs. • Exposure to multi-modal AI systems (text, image, or voice). Familiarity with Low-Code/No-Code tools (e.g., AppSheet) for workflow integration.