MLOps Engineer

About the role

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.

What you'll do

• 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
• Set up observability — drift detection (Evidently AI), Prometheus/Grafana, Azure Monitor

What you need

• 4–8 years in data science & ai
• MLOps
• Machine learning
• SQL
• Python
• Azure
• AWS
• Any graduate

Good to have

• LLMs
• Airflow
• Terraform
• Databricks
• Spark
• FastAPI