GenAI Engineer

HCLTech; Dadri; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

HCLTech is seeking a highly skilled Generative AI Developer to join the AI Store product team as the embedded GenAI expert. You will serve as the squad's single point of contact for all AI decisions, translating architectural decisions into production-grade implementations and uplifting team capabilities through training and mentorship.

Under the Generative AI Architect, you will drive complex GenAI and Agentic AI features for enterprise-grade solutions, integrating LLMs from OpenAI/Azure/Open

Qualifications

• Strong Python development for production-grade GenAI features.
• Hands-on experience with agentic AI and GenAI frameworks.
• Experience with RAG architectures, vector search, and multi-agent systems.
• Familiarity with OpenAI, Azure OpenAI, and other LLM APIs.
• Knowledge of vector databases and LLM evaluation/observability.

Responsibilities

• Act as embedded GenAI expert and single AI decision point for the squad.
• Design, develop, and optimize GenAI and Agentic AI apps and pipelines in Python.
• Implement LLMs into enterprise workflows, respecting enterprise standards.
• Design and deploy RAG pipelines and multi-agent orchestration.
• Build and evolve APIs, automation scripts, and AI pipelines on the AIForce platform.
• Coach squad members on GenAI concepts and best practices.

Skills

Python

Agentic AI

GenAI frameworks

RAG architectures

Vector databases

LLM APIs

Prompt engineering

Cloud platforms

Tools

LangChain

LlamaIndex

Hugging Face Transformers

AutoGen

CrewAI

Pinecone

FAISS

Job description

We are seeking a highly skilled Generative AI Developer to join the AI Store product team as an embedded AI expert within a squad.

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.

Working under the guidance of the Generative AI Architect, the developer will translate architectural decisions into production‑grade implementations, uplift team capabilities through training and mentorship, and independently drive the development of complex GenAI and Agentic AI features for enterprise‑grade solutions.

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.

Nice to Have

• Experience with MLOps tooling (MLflow, Azure ML Pipelines, or equivalent).
• Exposure to multimodal models (vision‑language models, speech‑to‑text integration).
• Prior experience working as an AI champion or tech lead within a cross‑functional product team.
• Familiarity with enterprise AI governance and compliance frameworks.