GenAI Developer / Lead

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.
Required Skills & Qualifications
• Strong proficiency in Python (3+ 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.
• Understanding of LLM fine-tuning approaches (RLHF, PEFT, LoRA) — practical experience will be added advantage.
• Knowledge of Safe AI principles, AI security, AI governance frameworks, and responsible AI development practices.
• Hands-on experience with developing AI solutions on any cloud platform
• Solid software engineering fundamentals: Git, CI/CD pipelines, automated testing, API design.
• Effective communication and mentoring skills — able to explain complex AI concepts to non-AI developers clearly.

Work arrangement
No