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

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Onsite

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

Tekgence

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

Skills

Google Cloud PlatformSQL QueriesDockerPythonData ScienceDevOpsTesting & QAPostgreSQLRedisDatabricksJenkinsGrafanaMachine LearningAIAWSPrometheusSAP ERPFastAPIPySparkApache AirflowTerraformAzure

1 month ago

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Important information


Contract type:

Permanent

Salary:

₹1,500,000 – ₹3,000,000 / year

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

17 September 2026

What they need


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 (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

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