Generative AI Engineer

HCLTech; Chennai District; On-site; INR 1,800,000 - 3,600,000; Full time

Job summary

HCLTech is seeking an AI Engineer to design, build, deploy, and operate production-grade Large Language Model solutions for enterprise applications. You will focus on engineering rigor, ensuring reliability, cost-efficiency, and security while turning LLM capabilities into scalable products.

The role emphasizes prompt engineering, RAG workflows, and integration with cloud environments, with collaboration across product and engineering teams to deliver robust AI solutions.

Qualifications

• Strong Python programming and backend API experience.
• Hands-on experience with LLM frameworks like LangChain or LlamaIndex.
• Experience building RAG pipelines with vector databases.
• Knowledge of Docker, Kubernetes, cloud platforms, and CI/CD.

Responsibilities

• Design and build LLM-powered applications using proprietary and open-source models.
• Implement prompt engineering, RAG, tool calling, and agent workflows.
• Deploy LLM solutions into cloud and enterprise environments with scalability and reliability.
• Build inference APIs, microservices, and CI/CD pipelines for AI applications.
• Monitor model quality, latency, cost, drift, and hallucinations in production.
• Fine-tune and enhance models using parameter-efficient techniques where required.
• Optimize inference performance using caching, batching, quantization, and prompt optimization.
• Ensure security, privacy, and responsible AI guardrails in all deployments.
• Collaborate with product, platform, and engineering teams to deliver enterprise AI solutions.

Skills

Python programming

LLM frameworks

RAG pipelines

Docker/Kubernetes/CI/CD

MLOps/monitoring

Tools

Docker

Kubernetes

Cloud platforms

CI/CD tooling

Job description

The AI Engineer will design, build, deploy, and operate production-grade Large Language Model (LLM) solutions. This role focuses on engineering rigor—turning LLM capabilities into reliable, scalable, and secure enterprise applications, and continuously improving them based on performance, cost, and user feedback.

Key Responsibilities

• Design and build LLM-powered applications using proprietary and open-source models.
• Implement prompt engineering, Retrieval-Augmented Generation (RAG), tool/function calling, and agent workflows.
• Deploy LLM solutions into cloud and enterprise environments with scalability and reliability.
• Build inference APIs, microservices, and CI/CD pipelines for AI applications.
• Monitor model quality, latency, cost, drift, and hallucinations in production.
• Fine-tune and enhance models using parameter-efficient techniques where required.
• Optimize inference performance using caching, batching, quantization, and prompt optimization.
• Ensure security, privacy, and responsible AI guardrails in all deployments.
• Collaborate with product, platform, and engineering teams to deliver enterprise AI solutions.

Required Skills & Experience

• Strong programming skills in Python; experience with backend APIs.
• Hands‑on experience with LLM frameworks (LangChain, LlamaIndex, or equivalent).
• Experience building RAG pipelines using vector databases.
• Knowledge of Docker, Kubernetes, cloud platforms, and CI/CD pipelines.
• Familiarity with LLMOps / MLOps tools and monitoring systems.

Experience Level

3–15 years of overall software or ML engineering experience.

Success Expectations

• Production-grade LLM solutions running reliably at scale.
• Continuous improvement in quality, cost-effectiveness, and user outcomes.
• Reusable AI engineering patterns that accelerate enterprise AI adoption.