AIAPPLYD

Technical Lead

HCLTech

Bengaluru, INonsitePosted Aug 12, 2026
Posting intelligenceActively listed

Skills

azure devopskubernetesprometheusterraformlangchainjenkinsgrafanadockergithubpythonopenaiazurecicdgooglecloudawsllmml

About the role

Bengaluru, Karnataka

Job Summary

Role Overview

We are looking for a highly skilled DevOps/MLOps/LLMOps Engineer to build, automate, and manage scalable AI/ML and Generative AI platforms. The ideal candidate will have experience operationalizing ML models and LLM-based applications, implementing CI/CD pipelines, monitoring AI systems, and enabling production-grade AI deployments.

Key Responsibilities

Design and maintain CI/CD pipelines for AI/ML and Generative AI applications.

Build and manage MLOps and LLMOps platforms for model training, deployment, monitoring, and governance.

Automate provisioning and deployment using Infrastructure as Code (IaC).

Implement observability, performance monitoring, and cost optimization for AI workloads.

Manage containerized workloads using Docker and Kubernetes.

Support deployment and lifecycle management of LLMs, RAG solutions, and Agentic AI applications.

Ensure security, compliance, and reliability of AI platforms.

Skill Requirements

5-8 years of experience in DevOps, MLOps, or Platform Engineering.

Strong expertise in Azure, AWS, or GCP.

Hands-on experience with Docker, Kubernetes, Terraform, GitHub Actions, Azure DevOps, or Jenkins.

Experience with ML lifecycle tools such as MLflow, Kubeflow, Azure ML, or SageMaker.

Knowledge of LLMOps, prompt management, model evaluation, vector databases, and RAG architectures.

Experience monitoring production AI systems using tools such as Prometheus, Grafana, Azure Monitor, or dynatrace

Strong scripting/programming skills in Python, Bash, or PowerShell.

Preferred Skills

Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and Agentic AI solutions.

Familiarity with LangChain, LangGraph, Semantic Kernel, or AutoGen.

Knowledge of Responsible AI, model governance, and AI security practices.

Azure, AWS, Kubernetes, or Terraform certifications.

Ideal Candidate

A hands-on engineer with proven experience building and operating enterprise-scale DevOps, MLOps, and LLMOps platforms , enabling reliable deployment, monitoring, governance, and optimization of AI and Generative AI solutions in production environments.

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