Role overview
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.
What we're looking for
- 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
- 8 years of software engineering experience with at least 2-3 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, Google ADK, is a must)
- Understanding of Git, CI/CD, DevOps , and production-grade GenAI deployment practices.
- Familiarity with Google Cloud Platform (GCP) — e.g. 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.
Tags & focus areas
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