TI
MLOps Engineer / Developer
Tranzeal Inc.
🇮🇳 India
On-site
3 weeks ago
- MLOps
- AI
- Devops
- AI/ML
- Machine Learning
- CI/CD
- Jenkins
- AKS
- FastAPI
- vLLM
- ONNX
- TensorRT
- Prometheus
- Grafana
- Azure Monitor
- DevSecOps
- Key Vault
- SonarQube
- Trivy
- Snyk
- Azure DevOps
- GitOps
- Git
- Databricks
- Delta Lake
- MLflow
- PySpark
- Docker
- Python
- Pydantic
- Bash
- YAML
- SQL
- Azure
- AWS
- GCP
- PostgreSQL
- Redis
- Helm
- Airflow
- Weights & Biases
- Terraform
- RBAC
- Ray
- Groovy
3 weeks ago
Location : ITPL, Bengaluru
work mode : 4 days work from office
Interview: AMAT interview will face to face - need local candidates
Experience Required: 4+ years
Salary : 24 LPA
Open New Demand # AMDIA00246 - AI Engineer + ML ops - 2
JD
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 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.
MLOps Engineer / Developer · Tranzeal Inc.