AI Ops

Introduction:

Our client is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio. The successful candidate will play a key role in migrating the organization from a fragmented ML toolchain to a unified, governed platform on AWS Landing Zone 2, covering the full ML lifecycle from data discovery through model deployment and monitoring.

Responsibilities:

• Set up SageMaker Unified Studio platform, including domain configuration, project provisioning, persona-based roles, and multi-environment promotion workflows.
• Build MLOps pipelines using SageMaker Pipelines for data extraction, preprocessing, training, evaluation, and model registration.
• Manage SageMaker Model Registry for cross-account model promotion, versioning, immutability, and lineage tracking.
• Configure MLflow experiment tracking for auto-logging of parameters, metrics, and artifacts.
• Set up identity and access management using Okta SSO, SailPoint entitlements, and persona-based execution roles.
• Build and manage model serving for real-time SageMaker endpoints and batch prediction workflows.
• Set up model monitoring for data drift, model drift, and performance degradation detection.
• Configure data catalog for searchable datasets, access-level visibility, and lineage tracking.
• Own platform operations including observability, logging, custom images, and instance availability.

Requirements:

Required:

• 15 years of software engineering experience focused on cloud infrastructure or ML platform operations. (Must)
• 5+ years hands-on experience with AWS, including expertise in Amazon SageMaker.( Must)
• 3+ years building and operating production MLOps pipelines.( Must)
• Experience with SageMaker Unified Studio or Studio Classic.
• MLflow or equivalent experiment tracking experience.
• SageMaker Pipelines or similar workflow orchestration knowledge.
• Infrastructure-as-Code experience with Terraform, CDK, or CloudFormation.
• IAM design for ML platforms including execution roles, service roles, cross-account access, and SSO/SAML.
• Experience with model serving, Snowflake data source, Kubernetes, and networking/security.

Preferred:

• Experience with SageMaker Unified Studio domain provisioning, custom blueprints, and project standardization.
• Knowledge of SageMaker Feature Store and Model Monitor.
• AWS Machine Learning Specialty certification.