MLOpsEngineer/Lead || Reston, VA (Onsite)

Role: MLOps Engineer/Lead

Location: Reston, VA (Onsite)

12+ Years

Seeking a Full Stack Machine Learning Engineer to support the ML Ops workstream within their broader model transformation program. This is a hands-on engineering role with strong emphasis on AWS, SageMaker, and end to end ML model operationalization.

Key Responsibilities

Build and operationalize ML models using AWS SageMaker.

Work closely with:

o Data Science team on model development and operationalization.

o 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 Technical Skills

Suppliers should ensure candidates meet the following must have requirements:

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).

o R is supported but secondary.

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.

Preferred Profile Traits

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.