AI Engineer + ML ops For Bangalore_ Final round will Be f2f

APPTAD, Inc.; Bengaluru; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

APPTAD, Inc. in Bangalore is hiring an MLOps Engineer to build and operate pipelines that move AI/ML models from exploration to production. This hands-on role bridges data science and enterprise deployment, focusing on packaging, CI/CD, model serving, and monitoring.

You will deploy APIs on AKS with FastAPI, implement observability, and apply DevSecOps practices. A strong background in Python, Databricks, and cloud platforms is required.

Qualifications

• 4+ years in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.
• CI/CD tooling including Jenkins, Azure DevOps; GitOps concepts and pre-commit workflows.
• Databricks ML pipelines (Delta Lake, MLflow), PySpark for data processing.

Responsibilities

• Build end-to-end ML/AI pipelines: data ingestion, training, evaluation, packaging, versioning, deployment.
• Develop and maintain CI/CD for multi-stage gates (Dev QA Production promotion).
• Deploy model-serving APIs on AKS using FastAPI and vLLM; optimize with ONNX/TensorRT.
• Set up observability: Evidently AI drift detection, Prometheus/Grafana, Azure Monitor.
• Apply DevSecOps: Key Vault, Managed Identity, SonarQube, Trivy/Snyk.
• Develop REST/WebSocket services using FastAPI.

Skills

ML/AI engineering

CI/CD pipelines

Databricks ML

FastAPI

Docker

AKS

Python

Cloud platforms

Education

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

Tools

Jenkins

Azure DevOps

GitOps

DL/ML tooling (Delta Lake, MLflow)

Job description

• Skills - AI Engineer + ML ops
• Work Mode (Onsite / Hybrid / Remote) Hybrid
• Work Location - Bangalore
• Years of experience required for this role 5+ Years

Note-F2F is mandatory for final round interview

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.