AI Engineer + ML ops

Zorba AI; Bengaluru; On-site; INR 2,400,000 - 4,000,000; Full time

Job summary

Zorba AI in Bengaluru seeks an MLOps Engineer to build and operate end-to-end ML pipelines, from data ingestion to production deployment. You will implement CI/CD pipelines, containerize services, and manage model serving APIs with FastAPI on AKS, while ensuring observability and security in a production environment.

The role bridges data science and enterprise deployment, requiring hands-on experience with MLOps, cloud storage, and multi-stage release processes.

Qualifications

• 4+ years in ML/AI engineering or DevOps with production MLOps experience.
• Proficient with CI/CD tools; Jenkins, Azure DevOps, GitOps concepts.
• Experience building Databricks ML pipelines and PySpark processing.

Responsibilities

• Build end-to-end MLOps pipelines for data ingestion, training, evaluation, packaging and deployment.
• Develop and maintain Jenkins CI/CD for multi-stage promotion (Dev→QA→Prod).
• Deploy model-serving APIs on AKS using FastAPI and vLLM; optimize with ONNX/TensorRT.
• Set up observability with Evidently, Prometheus and Grafana; monitor drift and performance.
• Apply DevSecOps practices: Key Vault, Managed Identity, SonarQube, Trivy/Snyk.
• Develop REST and WebSocket apps using FastAPI.

Skills

MLOps engineering

DevOps

Python

CI/CD

Docker

Kubernetes

FastAPI

Azure AWS GCP

Databricks

SQL/NoSQL

ONNX/TensorRT

vLLM

Education

B.E./B.Tech in CS/Software Engineering/Data Science

Tools

Jenkins

Azure DevOps

Databricks

FastAPI

Docker

AKS

Prometheus/Grafana

ONNX/TensorRT

Grafana

Job description

MLOps Engineer / Developer

Snapshot

Experience: 4–6 years in ML/AI engineering or DevOps

Reports To: MLOps Technical Lead / Manager, AIML

Education: B.E./B.Tech in CS, Software Engineering, or Data Science

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

Must-Have Skills

• 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.

Skills: azure,ml,data science,fastapi,pipelines