Machine Learning Engineer

Key Responsibilities:

ML Platform & Pipelines:

• Design and implement end-to-end ML pipelines for training, validation, packaging, and deployment.

• Create reusable templates and tooling for experimentation, feature consumption, and model lifecycle management.
CI/CD & Release Engineering:

• Build CI/CD for ML (tests, quality gates, approvals) using Azure Dev Ops/Git Hub Actions.

• Manage model registry, artifact versioning, and environment reproducibility.
Deployment, Monitoring & Reliability:

• Implement batch and real-time serving patterns; automate monitoring for drift, performance, and data quality.

• Establish SLOs/SLAs for ML services; lead incident response and root-cause analysis for ML production issues.
Cloud & Security:

• Operate ML infrastructure on Azure (preferred), including compute, networking, IAM, and secrets management.

• Apply governance: access controls, auditability, and compliance requirements.
Technical Qualifications Needed:

Education:

Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related field.
Mandatory

Required skills:

• 7+ years of experience in ML Ops, Dev Ops, Data Engineering. Data Science, or related roles.

• Hands-on experience with Azure ML and/or Databricks, MLflow, and asset bundles.

• Kubernetes and orchestration experience; familiarity with model serving frameworks.

• Strong Python and SQL skills; experience with automated testing and observability.

• Experience with monitoring/alerting (e.g., Azure Monitor, Prometheus/Grafana).
Desired

Skills:

• Infrastructure-as-Code (Terraform/Bicep) and policy-as-code experience.

• Experience with feature stores and data quality frameworks.

• Experience supporting regulated data environments and security reviews.
Certifications
Azure Dev Ops Engineer Expert, Azure Data Engineer Associate, or similar certifications preferred.