MLOps Engineer / Developer

Sass Innovations; Bengaluru; On-site; INR 3,000,000 - 5,500,000; Full time

Job summary

Sass Innovations in Bengaluru seeks an MLOps Engineer to build and operate pipelines that take AI/ML models from experimentation to production packaging, CI/CD delivery, model serving, and monitoring. A hands-on role bridging data science and enterprise deployment.

Responsibilities include end-to-end pipeline development, Jenkins CI/CD maintenance, deploying model-serving APIs on AKS with FastAPI, and establishing observability with Evidently AI and Prometheus/Grafana, plus applying DevSecOps

Qualifications

• 4+ years in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.

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

Skills

ML/AI engineering

DevOps

CI/CD tooling

Databricks ML pipelines

Model serving

Python

Cloud (Azure/AWS/GCP)

PostgreSQL/Redis

Containerization (Docker)

AKS/Kubernetes

Education

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

Tools

Jenkins

Azure DevOps

GitOps

Git

Docker

AKS

FastAPI

ONNX/TensorRT

Job description

MLOps Engineer / Developer

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).