AI Ml Ops Engineer

HCL Technologies Limited; Bengaluru; Hybrid; INR 1,500,000 - 3,000,000; Full time

Job summary

HCL Technologies Limited is seeking an experienced ML/AI engineer to build and operate MLOps pipelines, bringing models from experimentation to production packaging and deployment.

You will bridge data science and enterprise deployment, implementing CI/CD, model serving on AKS with FastAPI, and comprehensive observability to ensure reliable operation.

Qualifications

• 4+ years in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.
• Strong CI/CD tooling knowledge and production deployment workflows.
• Experience with Databricks ML pipelines and PySpark for data processing.

Responsibilities

• Build end-to-end ML/AI pipelines: data ingestion, training, evaluation, packaging, deployment.
• Develop and maintain Jenkins CI/CD for multi-stage gates and promotions.
• Deploy model-serving APIs on AKS using FastAPI and optimize with ONNX/TensorRT.
• Set up observability: metrics and drift detection with Evidently AI, Prometheus, Grafana.
• Apply DevSecOps practices: Key Vault, Managed Identity, SonarQube, Trivy/Snyk.
• Develop REST/WebSocket services using FastAPI.

Skills

ML/AI engineering

DevOps

Jenkins

Azure DevOps

GitOps

Databricks

PySpark

FastAPI

Docker

Kubernetes

AKS

Cloud (Azure/AWS/GCP)

PostgreSQL

Prometheus/Grafana

Evidently AI

Trivy/Snyk

Terraform

ONNX/TensorRT

Python

Bash

Tools

Jenkins

Azure DevOps

GitOps

Databricks

Docker

AKS

FastAPI

PySpark

ONNX/TensorRT

Prometheus

Grafana

Helm

vLLM

Evidently AI

Job description

Job Summary

About the Role
Build and operate the MLOps pipelines that take AI/ML and GenAI models from experimentation to production packaging, CI/CD delivery, model serving, and monitoring. A hands-on engineering role bridging data science and enterprise deployment.

Key Responsibilities

• Build end-to-end pipelines — data ingestion, training, evaluation, packaging, versioning, and deployment.
• Develop and maintain Jenkins CI/CD for Dev QA Production promotion with multi-stage gates.
• Deploy model-serving APIs on AKS using FastAPI and vLLM; apply ONNX/TensorRT optimizations.
• Set up observability — drift detection (Evidently AI), Prometheus/Grafana, Azure Monitor.
• Apply DevSecOps practices — Key Vault, Managed Identity, SonarQube, Trivy/Snyk.
• Application Development – REST, WebSocket Frameworks using FastAPI

Skill Requirements

• 4+ years in ML/AI engineering or DevOps with hands‑on production MLOps pipeline experience.
• CI/CD tooling: CI tooling (UV, Ruff, Pyrefly), Jenkins (strong), Azure DevOps, GitOps concepts; Git and pre-commit workflows.
• Databricks ML pipelines (Delta Lake, Workflows, MLflow), Asset Bundles and PySpark for data processing.
• Model serving: FastAPI, Docker, AKS; exposure to vLLM and ONNX/TensorRT optimization.
• Python (strong — FastAPI, Pydantic, async), Bash, YAML/SQL scripting.
• Cloud knowledge – Azure/AWS/GCP Storage, AI related services.
• Databases and storage: PostgreSQL, Redis, ADLS Gen2.
• Understanding of containerization, Helm, and infrastructure automation.

Nice to Have

• Airflow, DVC, and experiment tracking (W&B / Comet ML).
• Terraform, KEDA, Azure APIM, and AAD RBAC configuration.
• LLM fine-tuning pipelines; Ray Serve or BentoML exposure.
• Groovy (Jenkinsfile).
• Manufacturing or semiconductor domain experience.