Senior ML Engineer

Job Description

description 5+ years with Azure (ML pipeline components), Azure Databricks, Azure DevOps. Proven experience of design and deployment of end-to-end ML pipelines. Experience with building infrastructure for classic DS models and/or LLM/SLMs. 2+ years with orchestration (e.g., Kubeflow, Airflow, Azure Data Factory) and CI/CD for ML. Experience deploying containerized ML solutions (e.g. Docker/Kubernetes). Knowledge of model and data versioning (e.g. MLflow, DVC). Knowledge of MLSecOps (security in the context of MLOps) Experience with Infrastructure as a Code (e.g., Terraform, CloudFormation). Essential functions List of identified areas for improvements for existing PepsiCo project Recommendation on: MLOps practice MLSecOps Sample templates and pipelines Recommendation on the infrastructure for: Classic DS models LLM solution, primarily focused on fine tuning SLM by domain Recommendation on Data Strategy: JIT access to data for the period of training with security guardrails (access, data masking, etc.) Qualifications Azure Databricks, Azure DevOps. ML Ops ML SecOps Classic DS Model Would be a plus Knowledge of MLOps for LLM/SLM Experience in ML system/architecture design (load balancing, caching, failover). Knowledge in building scalable, resilient ML architectures. Cross-team collaboration experience (data science, engineering, DevOps). Experience with monitoring/logging for production models (e.g. Prometheus, Grafana, ELK stack).