AI/ML Engineers Job Type

Full-time/OSP Role Summary

3–10+ years of overall software engineering experience, with significant hands-on experience in Generative AI, LLM application development, Agentic AI, RAG, and Python-based development. We are looking for a highly skilled GenAI / Agentic AI Engineer to design, develop, deploy, and scale enterprise-grade AI applications and intelligent agents. The ideal candidate should have solid hands-on experience in Python development, Generative AI, Large Language Models (LLMs), Agentic AI architectures, Retrieval-Augmented Generation (RAG), prompt engineering, LLM evaluation, GenAI assurance, and cloud-native deployment.

The candidate will be responsible for building production-ready GenAI solutions that can interact with enterprise data, use multiple LLMs and tools, perform reasoning and task orchestration, and provide reliable, explainable, secure, and scalable responses. Strong experience with Google Cloud Platform (GCP) is preferred, particularly with Google Cloud's AI/ML and GenAI ecosystem.

Experience with AWS or Azure is also acceptable where the candidate demonstrates equivalent GenAI architecture and deployment expertise.

Experience working directly with Google clients/customer environments is highly desirable.



Exposure to Google internal/proprietary engineering, AI, development, evaluation, or productivity tools will be considered a significant value-add. The role requires an engineer who can move beyond GenAI experimentation and build enterprise-grade, measurable, governed, and production-ready AI systems. Key Responsibility

- Hands-on GenAI & Python based Development – Design, code, test, and deploy production-grade GenAI/Agentic AI solutions using Python; strong Python development is mandatory.
- Agentic AI – Develop single/multi-agent solutions using Google ADK, LangChain, LangGraph, CrewAI, or equivalent, including tool/function calling, orchestration, memory, state management, workflows, and guardrails.
- Prompt Engineering – Design and optimize advanced prompts using Chain-of-Thought (CoT), Tree-of-Thought (ToT), Few-Shot, Zero-Shot, Instruction-based, Role-based, ReAct, structured prompting, and other techniques.
- LLM Engineering – Work with multiple LLMs including Gemini, OpenAI, Claude, Llama, Mistral, and other proprietary/open-source models; implement model selection, routing, fallback, structured output, and function calling..

📌 Lead Software Engineer (Hyderabad)
🏢 Virtusa
📍 Hyderabad