Role Description
ML Engineer I
Who We Are
At UST, we help the world’s best organizations grow and succeed through transformation. Bringing together the right talent, tools, and ideas, we work with our client to co-create lasting change. Together, with over 30,000 employees in 25 countries, we build for boundless impact—touching billions of lives in the process. Visit us at .
The Prospect
8+ years in ML/AI engineering or DevOps (incl. 4+ yrs production MLOps)
Education: Master's (preferred) or Bachelor's in CS, Software Engineering, or Data Science
About the Role Own the complete MLOps backbone for AI/ML and GenAI solutions — from model packaging and CI/CD delivery through production monitoring. Bridge data-science experimentation and enterprise-scale deployment, and set engineering standards for the Enterprise AI MLOps practice. Key Responsibilities
Design end-to-end pipelines — ingestion, training, evaluation, packaging, versioning, deployment, and monitoring.
Own Jenkins CI/CD (Declarative/Scripted) for Dev → QA → Production with multi-stage gates.
Deploy model-serving APIs on AKS (FastAPI, vLLM); apply ONNX/TensorRT and blue/green rollouts.
Build observability stacks — drift detection (Evidently AI), Prometheus/Grafana, Azure Monitor.
Enforce DevSecOps — zero-credential pipelines via Key Vault + Managed Identity, SonarQube, Trivy/Snyk.
Set MLOps engineering standards and mentor engineers across the practice
What Are We Looking For
7+ years in ML/AI engineering or DevOps with 4+ years operating production MLOps pipelines.
CI/CD & Dev tooling: Jenkins (expert), Azure DevOps, ArgoCD (GitOps); UV, Ruff, Pyrefly, pre-commit.
MLOps & data: Databricks end-to-end (Delta Lake, Workflows, MLflow, Unity Catalog), Airflow, DVC, PySpark.
Serving & infra: FastAPI, ONNX/TensorRT, AKS, Helm, Docker, KEDA, Azure APIM, Terraform.
Security: Key Vault, AAD RBAC, Managed Identity; zero-credential pipeline design
Languages: Python (expert — FastAPI, Pydantic, async), Groovy
📌 AI Engineer Lead- (Bengaluru)
🏢 UST
📍 Bengaluru