MLData Engineer
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Job Location:
Reston, VA - USA
Monthly Salary:
Not Disclosed
Posted on:
9 hours ago
Vacancies:
1 Vacancy
Job Summary
(Local candidates only as F2F Interview is must)
Project Overview
The client is seeking an experienced ML/Data Engineer to lead end-to-end machine learning operations including model development experiment tracking and lifecycle management. The role focuses on ensuring model quality governance alignment and scalable engineering practices across platforms such as Domino and Amazon SageMaker.
Key Responsibilities
- Manage monitoring tracking and maintenance of ML models across Domino and SageMaker platforms
- Implement MLflow for experiment tracking including parameters metrics artifacts and lineage
- Build and maintain scalable data pipelines for training validation and inference
- Develop custom evaluation metrics model explainability components and bias/fairness testing frameworks
- Package and deploy ML models supporting lifecycle transitions across environments
- Collaborate with data scientists engineering teams and governance stakeholders to ensure compliance and operational readiness
- Design and structure datasets for analytical and application use
- Normalize databases and ensure data structures meet application requirements
- Integrate data from multiple sources into consistent machine-readable formats
Required Qualifications
- Bachelors degree in Computer Science Information Systems or a related field
- 15 years of relevant experience in data engineering or machine learning engineering
- Postgraduate degree and/or professional certifications (preferred)
Technical & Soft Skills
Technical Skills:
- Strong experience with AWS and machine learning engineering
- Proficiency in Python and MLflow
- Hands-on experience with Domino and Amazon SageMaker SDKs
- Expertise in feature engineering and building scalable data pipelines
- Experience with model validation explainability and bias/fairness tools
- Strong knowledge of SQL data modeling and data processing tools such as Spark Hive and Airflow
- Familiarity with Git-based workflows version control and MLOps practices
- Experience working with relational databases NoSQL systems and data lakes
Soft Skills:
- Strong collaboration skills across cross-functional teams
- Ability to work with governance and compliance stakeholders
- Detail-oriented with a focus on model quality and operational excellence
