ML/AI Engineer

GyanSys Inc.; Bengaluru; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

GyanSys Inc. is seeking an experienced MLOps/AI engineering professional in Bengaluru to build and operate end-to-end ML pipelines, from data ingestion to production deployment.

You will manage CI/CD (Jenkins, Azure DevOps) and model-serving APIs on AKS with FastAPI, while establishing robust observability and DevSecOps practices. This hands-on role bridges data science and enterprise deployment.

Qualifications

• 4+ years in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.
• CI/CD tooling experience including Jenkins, Azure DevOps, GitOps concepts, and pre-commit workflows.
• Experience with Databricks ML pipelines, Delta Lake, MLflow, and PySpark for data processing.
• Model serving using FastAPI, Docker, AKS; exposure to vLLM and ONNX/TensorRT optimizations.
• Proficiency in Python (FastAPI, Pydantic), Bash, YAML/SQL scripting.
• Cloud storage and AI services across Azure/AWS/GCP, plus databases like PostgreSQL/Redis/ADLS Gen2.
• Understanding of containerization, Helm, and infra automation.

Responsibilities

• Build end-to-end ML/AI pipelines from ingestion to deployment.
• Develop and maintain Jenkins CI/CD for multi-stage promotion including production gates.
• Deploy model-serving APIs on AKS using FastAPI and vLLM with optimizations.
• Set up observability using Evidently AI, Prometheus, Grafana, and Azure Monitor.
• Apply DevSecOps practices with Key Vault, Managed Identity, SonarQube, Trivy/Snyk.
• Develop REST and WebSocket services using FastAPI.

Skills

ML/AI engineering

DevOps

CI/CD tooling

Jenkins

Azure DevOps

GitOps concepts

Python (FastAPI)

Docker

AKS

vLLM

Tools

Databricks ML pipelines

Delta Lake

MLflow

PySpark

ONNX/TensorRT

Evidently AI

Prometheus/Grafana

Azure Monitor

Key Vault

Snyk/Trivy

Helm

Job description

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.