AI Engineer + ML ops -4+ Years-work from Office-Bangalore (Bengaluru)

AI Engineer + ML ops -4+ Years-work from Office-Bangalore (Bengaluru)

18 Sep
|
Tekgence
|
Bengaluru

18 Sep

Tekgence

Bengaluru

Position : AI Engineer + ML ops

Exp: 4+ Years SAP CPI

Client: One of our Prestigious client.

Mode of hiring :Full Time /Permanent

Budget: 20LPA-35 LPA (Including VP)

Location: Bangalore

Mode of Work: Work from Office from day 1 (Client location)

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 (robust — 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

📌 AI Engineer + ML ops -4+ Years-work from Office-Bangalore (Bengaluru)
🏢 Tekgence
📍 Bengaluru

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