🚀 We’re Hiring: ML / Data Engineer (MLOps & Pipeline Engineering)
Location: Reston, VA (5 Days Onsite) Duration: Full-time / Contract Interview Process: In-person (Herndon, VA)
The Opportunity Are you an engineer who loves bridging the gap between data science and production? We are looking for a highly skilled ML / Data Engineer to take the reins on our end-to-end machine learning operations.
In this role, you won''t just be maintaining systems; you will be the driving force behind our model lifecycle quality, governance, and engineering excellence across Amazon SageMaker and Domino. If you are passionate about building scalable data pipelines, advancing AI fairness, and mastering MLOps, we want you on our team.
What You’ll Build & Own
End-to-End MLOps: Take full ownership of monitoring, tracking, and maintaining machine learning models deployed across Domino and SageMaker.
Pipeline Architecture: Architect and maintain highly scalable, robust data pipelines tailored for training, validation, and inference.
Experiment Tracking: Implement and manage MLflow for seamless parameter tracking, metrics monitoring, artifact management, and complete end-to-end data lineage.
Responsible AI: Develop custom evaluation metrics, explainability components, and rigorous fairness/bias testing frameworks to ensure our models are both powerful and ethical.
Deployment & Lifecycle: Package models for flawless deployment and support seamless lifecycle transitions across multiple environments.
Cross-Functional Collaboration: Partner directly with brilliant Data Scientists, Engineering squads, and Governance teams to ensure compliance and total operational readiness.
The Tech Stack & Skills You Bring
Core Engineering: Strong, hands-on background in AWS infrastructure and Machine Learning Engineering.
Languages & Tools: High proficiency in Python and MLflow.
Platform Expertise: Deep, practical experience utilizing Domino and Amazon SageMaker SDKs.
Data Mastery: Proven track record with advanced feature engineering and building scalable data pipelines.
Model Quality: Solid understanding of model validation, explainability, and bias/fairness tooling.
Best Practices: Comfortable navigating Git-based workflows, version control, and modern MLOps standards.
Why Join Us? You will be stepping into a high-impact role where your engineering decisions directly influence how machine learning models scale and operate in the real world. You will work with a modern tech stack alongside a team that values innovation, clean code, and responsible AI.
Interested in building the future of our ML infrastructure? Apply below or send a direct message to connect!
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