GenAI Expert

HCL Technologies Limited; Pune District; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

HCL Technologies Limited is seeking a Generative AI Developer to join the AI Store product team in Pune as an embedded AI expert within a squad of software engineers.

The role blends hands-on development, training, and technical leadership, driving GenAI initiatives, guiding best practices, and delivering end-to-end AI features across production pipelines.

Qualifications

• Proficiency in Python with hands-on experience building production-grade GenAI applications.

Responsibilities

• Act as embedded GenAI expert and single point of contact for AI/GenAI decisions.
• Design, develop and optimize production-grade GenAI apps and pipelines in Python.
• Integrate OpenAI, Azure OpenAI, and other models into enterprise workflows.
• Build RAG pipelines and multi-agent orchestration systems.
• Mentor squad members on GenAI concepts and best practices.
• Evaluate LLM performance, prompts, and model fine-tuning.

Skills

Python

GenAI

Prompt engineering

LLM evaluation

Communication

Mentoring

Tools

LangChain

LlamaIndex

Hugging Face Transformers

AutoGen

CrewAI

Pinecone

Weaviate

Azure AI Search

FAISS

Chroma

Job description

HCLTech is looking for highly skilled Generative AI Developer to join the AI Store product team as an embedded AI expert within a squad of 7–10 traditional software engineers (backend, frontend, and full-stack developers).

This is not a conventional developer role — the ideal candidate will serve as the squad's single point of contact for all things AI and Generative AI: Hands-on developer, Internal trainer, Technical guide, and Implementation 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.