Job Description

Job Summary

This role is responsible for implementing advanced solutions in standard cell characterization, ensuring product and sustenance delivery meets defined technology standards. The position drives conceptualization, technical design, and hands-on implementation of AI usecases

Key Responsibilities

• Act as the squad's embedded GenAI expert and single point of contact for all AI/GenAI-related decisions, implementation, and guidance.
• Design, develop, and optimize production-grade GenAI and Agentic AI applications, services, and pipelines in Python.
• Work under the GenAI Architect to interpret architectural blueprints and implement complex GenAI components, ensuring alignment with enterprise standards.
• Integrate Large Language Models (LLMs) — including OpenAI, Azure OpenAI, Hugging Face, Anthropic, and Cohere — into enterprise workflows and products.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestration systems, and Agentic AI flows.
• Build, maintain, and evolve APIs, automation scripts, and AI pipelines on the AIForce platform.
• Train and mentor squad members (backend, frontend, full-stack developers) on GenAI concepts, tools, frameworks, and best practices — enabling the broader team to actively contribute to AI feature development.
• Conduct LLM performance evaluation, prompt optimization, and model fine-tuning as required.
• Champion Safe AI, AI governance, and responsible AI development practices across the squad.
• Monitor, test, and troubleshoot deployed GenAI models and services in production environments.
• Stay current with emerging GenAI frameworks, LLM advances, and industry trends; proactively assess and introduce relevant innovations.

Skill Requirements

• Strong proficiency in Python (2+ years), including experience building production-grade applications.
• Proven hands-on experience with Agentic AI and GenAI frameworks: LangChain, LlamaIndex, Hugging Face Transformers, AutoGen, CrewAI, or similar.
• Demonstrated experience designing and implementing RAG architectures, vector search pipelines, and multi-agent systems.
• Familiarity with LLM APIs: OpenAI, Azure OpenAI, Anthropic, Cohere, and open-source models.
• Experience with vector databases (e.g., Pinecone, Weaviate, Azure AI Search, FAISS, Chroma).
• Strong knowledge of prompt engineering, chain-of-thought techniques, and LLM evaluation/ observability methods.

Other Requirements

BE/BTech Computer