ML Engineer - AWS, Machine Learning, SageMaker, Git, Terraform, MLOps
Type: W2 With Benefits - No C2C
Location: Washington DC - 2 or 3 days per week onsite
Key Responsibilities
- Build and operationalize ML models using AWS SageMaker.
- Work closely with:
- Data Science team on model development and operationalization.
- Technology team to validate platform integration and functionality.
- Act in a product owner like capacity for the ML Ops platform—ensuring alignment with model development needs.
- Validate end to end ML Ops integrations (SageMaker, GitLab, Terraform, etc.).
- Help close gaps between data science and technology teams during initial platform implementation.
- Support model retraining, deployment strategies, and iterative model lifecycle processes.
Required Core Technical Competencies
- Strong AWS experience, especially SageMaker (processing, MLflow model registry, etc.).
- ML Ops expertise, including understanding of DevOps principles.
- Experience integrating ML systems with GitLab, Terraform, and similar tools.
- Python proficiency is mandatory (primary language for ML at client).
- Strong data engineering skills:
o Feature engineering
o Data Sourcing
o Working with large datasets
- Solid understanding of the end-to-end model development lifecycle.
Soft Skills:
- Ability to lead directionally, not just follow instructions.
- Comfortable working cross functionally across business, technology, and data science.
- Hands on approach; not a conceptual-only role.
Benefits:
SES hires W2 benefitted and non-benefitted consultants. Our contract employee benefits include group medical dental vision life LT and ST disability insurance, 21 days of accrued paid time off, 401k, tuition reimbursement, performance bonuses, paid overtime, and more.
Please contact me to discuss the details of this position further.
*Please forward resume directly to for immediate consideration - rstarinieri at sesc .com
I look forward to speaking with you soon!
Robin Starinieri
Director of Recruiting
Systems Engineering Services
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