Role overview
Noblis and our wholly owned subsidiaries, Noblis ESI and Noblis MSD, take on some of the nation’s toughest challenges, delivering advanced solutions to our customers’ most critical missions. We bring together leading scientific, engineering, and management expertise in a culture grounded in objectivity and collaboration, ensuring our work creates lasting impact across federal missions.
We work with a broad range of government agencies in the defense, intelligence, and federal civilian sectors. Learn more and find opportunities at careers.noblis.org
Why Work at Noblis
At Noblis, we share a passion for excellence and innovation, and we create an environment where people can do meaningful work while maintaining the balance that keeps them energized and fulfilled. We seek out individuals with a natural curiosity and desire to collaborate and learn. We believe our people are our greatest strength, and we consistently seek exceptionally skilled, mission‑driven professionals who care deeply about doing work that enriches lives and makes our nation safer.
Noblis has earned numerous workplace awards for our culture, our commitment to employee well‑being, and our dedication to meaningful, impactful work. We also maintain a drug‑free workplace.
Remote/hybrid status is subject to change based on Noblis and/or government requirements.
Commitment to Non-Discrimination:
All qualified applicants will receive consideration for employment without regard to race, color, ethnicity, sex, age, national origin, religion, physical or mental disability, pregnancy/childbirth and related medical conditions, veteran or military status, or any other characteristics protected by applicable federal, state, or local law.
If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact us.
EEO is the Law | E-Verify | Right to Work
Total Rewards:
At Noblis we recognize and reward your contributions, provide you with growth opportunities, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, and work-life programs. Our award programs acknowledge employees for exceptional performance and superior demonstration of our service standards. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in our benefit programs. Other offerings may be provided for employees not within this category. We encourage you to learn more about our total benefits by visiting the Benefits page on our Careers site.
Compensation at Noblis is determined by various factors, including but not limited to, the combination of education, certifications, knowledge, skills, competencies, and experience, internal and external equity, location, clearance level, as well as contract-specific affordability, organizational requirements and applicable employment laws. The projected compensation range for this position is based on full time status. For part time or on-call staff, compensation is proportionately adjusted based on hours worked. While monetary compensation is important, it's just one component of Noblis’ total compensation package.
Posted Salary Range: USD $160,800.00 - USD $251,325.00 /Yr.
Responsibilities
- Model Development & Deployment Design, develop, and containerize machine learning (ML) models using modern frameworks and tools, including PyTorch, Ray, Docker, and FastAPI. Deploy, manage, and scale production ML workloads on Kubernetes. Integrate AI/ML capabilities into full-stack applications using Python-based backend services and JavaScript frontend technologies. Ensure model reliability, performance, and maintainability throughout the deployment lifecycle.
- Design, develop, and containerize machine learning (ML) models using modern frameworks and tools, including PyTorch, Ray, Docker, and FastAPI.
- Deploy, manage, and scale production ML workloads on Kubernetes.
- Integrate AI/ML capabilities into full-stack applications using Python-based backend services and JavaScript frontend technologies.
- Ensure model reliability, performance, and maintainability throughout the deployment lifecycle.
- Infrastructure & Operations Architect and implement cloud-native ML infrastructure on AWS. Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring. Deploy and support AI/ML systems within secure, classified, and high side environments.
- Architect and implement cloud-native ML infrastructure on AWS.
- Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring.
- Deploy and support AI/ML systems within secure, classified, and high side environments.
- Technical Leadership Evaluate and adopt state-of-the-art AI/ML models, frameworks, and emerging technologies. Architect scalable and resilient infrastructure to support evolving AI/ML workloads and mission requirements. Establish and promote best practices for production-grade machine learning (ML) systems, including security, observability, and governance. Provide technical guidance and thought leadership across AI/ML initiatives and engineering teams.
- Evaluate and adopt state-of-the-art AI/ML models, frameworks, and emerging technologies.
- Architect scalable and resilient infrastructure to support evolving AI/ML workloads and mission requirements.
- Establish and promote best practices for production-grade machine learning (ML) systems, including security, observability, and governance.
- Provide technical guidance and thought leadership across AI/ML initiatives and engineering teams.
Basic qualifications
- Active Top Secret/SCI (TS/SCI) clearance with a current Polygraph
- Bachelor’s degree with 8 years of related experience; OR Master's degree with 7 years of related experience; OR associate’s degree with 11 years of related experience; OR High School diploma/GED with 14 years of related experience
- Production experience deploying ML models, including LLMs
- Strong proficiency with ML frameworks and containerization (e.g., PyTorch, Docker, Kubernetes)
- Full-stack development experience (e.g., Python, JavaScript)
- Working knowledge of AWS cloud services
- Demonstrated MLOps/DevOps implementation experience
- U.S. Citizenship is required
- AWS Certification, including AWS Certified DevOps Engineer or AWS Certified Solutions Architect Certification