Machine Learning Engineer
Listed on 2026-07-23
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IT/Tech
Data Engineering, Cloud Computing: Infrastructure & Operations, Machine Learning/ ML Engineer, AWS
Job Location:
Plano, TX (100% Onsite) - LOCAL Only
Project Duration: 12+ months with possible extension
(W2 Position only- NO C2C)
UPDATE- MLOPS with at least 2+ years experience in Sagemaker Studio Classic is mandatory here
Job SummaryClient is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS Sage Maker Unified Studio. You will migrate 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.
This position is with Enterprise Analytical Data & Integration Team and the hiring manager is looking to onboard MLOpsPlatform Engineer (Sagemaker) who is expert in Sagemaker (key skillset) and AWS.
What we’re looking for Roles- Set up Sage Maker Unified Studio platform — domain configuration, project provisioning, persona‑based roles, and multi‑environment (Dev, Prod‑UAT, Prod) promotion workflows
- Build MLOps pipelines using Sage Maker Pipelines — data extraction from Snowflake, preprocessing, training, evaluation, and model registration
- Manage Sage Maker Model Registry — cross‑account model promotion, versioning, immutability, and lineage tracking
- Configure MLflow experiment tracking — auto‑logging of parameters, metrics, and artifacts
- Set up identity and access management — Okta SSO, SailPoint entitlements, persona‑based execution roles, service roles for pipelines
- Build model serving — real‑time Sage Maker endpoints and batch prediction workflows
- Configure data catalog — searchable datasets, access‑level visibility, access‑request workflows, lineage
- Own platform operations — observability (Cloud Watch, Datadog), logging, custom images, instance availability
5+ years hands‑on with AWS, including deep expertise in Amazon Sage Maker (Studio, Pipelines, Model Registry, Endpoints, Feature Store)
3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollback
Experience with Sage Maker Unified Studio or Studio Classic — domain/project setup, blueprints, multi‑tenant configuration
Unified Studio is preferred to have but Classic is must have.
MLflow, Terraform/CDK/Cloud Formation
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