What are the responsibilities and job description for the Machine Learning Operations Engineer position at SAIC?
Job ID: 2510599
Location: FORT BELVOIR, VA, US
Date Posted: 2025-10-07
Category: Engineering and Sciences
Subcategory: Machine Learning Engineer
Schedule: Full-time
Shift: Day Job
Travel: Yes, 10 % of the Time
Minimum Clearance Required: TS/SCI
Clearance Level Must Be Able to Obtain: TS/SCI with Poly
Potential for Remote Work: No
Description
SAIC is seeking a Machine Learning Operations Engineer to join our team in Fort Belvoir, Virginia.
The MLOps Engineer will be responsible for streamlining the deployment, monitoring, and maintenance of machine learning (ML) models and systems within the Army Intelligence & Security Enterprise (AISE). This role will focus on implementing MLOps practices to ensure scalability, reliability, and efficiency of AI/ML solutions in operational environments. The MLOps Engineer will collaborate with data scientists, ML engineers, and cloud engineers to deliver mission-critical AI capabilities.
Job Duties:
- Design and implement MLOps pipelines to automate the deployment and monitoring of ML models.
- Develop tools and frameworks to support continuous integration and continuous delivery (CI/CD) for AI/ML systems. · Troubleshoot identified issues.
- Collaborate with data scientists and ML engineers to optimize model performance and scalability.
- Monitor deployed models for performance, drift, and reliability, and implement retraining workflows as needed.
- Ensure compliance with security protocols and governance policies for AI/ML systems.
- Stay updated on advancements in MLOps practices, tools, and technologies.
- Support the operational integration of AI/ML solutions to enhance intelligence capabilities.
Qualifications
Required Education:
Bachelors and five (5) years or more experience; Masters and three (3) years or more experience; PhD or JD and zero (0) years or more experience; four (4) years of experience considered in lieu of degree.
Qualifications:
Five (5) or more years of experience in MLOps, DevOps, systems engineering, or related roles.
Proficiency in programming languages such as Python or Bash.
Strong understanding of MLOps tools and frameworks (e.g., MLflow, Kubeflow, TensorFlow Extended).
Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
Experience with cloud-based AI/ML platforms and tools (e.g., AWS, Azure, Google Cloud).
Experience with leveraging Large Language Models (LLMs) such as GPT, or similar frameworks for tasks like natural language processing (NLP), text generation, summarization, or conversational AI.
Excellent problem-solving and collaboration skills.
Desired Qualifications:
Advanced degree (Master's or PhD) in Machine Learning, Artificial Intelligence, or a related field.
Experience with monitoring tools for AI/ML systems (e.g., Prometheus, Grafana).
Familiarity with Army Intelligence Enterprise data requirements and workflows.
Knowledge of version control systems and practices for ML models and data.
Clearance:
Candidates must have an active TS/SCI with the ability to obtain a TS/SCI with Poly
