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.
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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
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.
Good-to-Have
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