Responsibilities
Peraton is seeking a Machine Learning Operations (MLOps) Engineer to support the Multi Domain Task Force at Joint Base Lewis McChord, WA.
As an MLOps Engineer, you will apply your understanding of data networks, databases, and cloud architecture to collect, gather, process, store, and provide timely data.
This work will be CI / CD and MLOps based, supporting the data-driven decision process.
Tasks include :
- Requirements Collaboration : Collaborates closely with customers, machine learning, and data science teams to thoroughly understand data science project requirements and objectives.
- MLOps Leadership : Champions practices and tools for managing the end-to-end lifecycle of machine learning models. Includes : Data Versioning and Management, Model Versioning, Model Training and Validation Pipelines, Model Deployment and Monitoring, Infrastructure as Code (IaC), Collaboration and Governance, Security and Compliance.
- CI / CD Support : Takes the lead in implementing robust Continuous Integration (CI) and Continuous Deployment (CD) code management pipelines for machine learning models.
Ensures seamless automation of critical development stages, resulting in high-quality software releases and reduced errors.
- Model Lifecycle Management : Ensures proper model training, validation, deployment, and ongoing monitoring. Maintains lifecycle model health and performance.
- Infrastructure Design : Establishes infrastructure tailored to project requirements and constraints, enabling efficient model development and deployment.
Qualifications
Required :
- Minimum of 12 years with BS / BA; Minimum of 10 years with MS / MA; Minimum of 7 years with Ph.D. Will consider HS+16 years of experience.
- Able to work on high-visibility or mission critical aspects of a given program and performs all functional duties independently.
Must be able to oversee the efforts of less senior staff and / or be responsible for the efforts of all staff assigned to a specific job.
Requires programming Languages : Proficient in Python, R, SQL, and scripting, experience with Deep Learning platforms (e.
g., PyTorch, TensorFlow, Jupyter Notebook), and version control using Git.
- Requires an understanding of MLOps & MLFlow principles.
- Additional Skills : Understanding of containerization (Docker, Kubernetes), ability to effectively collaborate with data scientists and engineers, ability to drive Machine Learning Operations (MLOps) process to product incremental model improvements.
- Problem-Solving and Statistical Knowledge : Strong problem-solving skills as applied to Machine Learning issues, comfortable with data manipulation, analysis, and scripting.
- Must have Secret or above to start. Must have the ability to obtain TS / SCI security clearance for continued employment.
Preferred
TS SCI
Benefits :
Peraton offers enhanced benefits to employees working on this critical National Security program, which include heavily subsidized employee benefits coverage for you and your dependents, 25 days of PTO accrued annually up to a generous PTO cap and eligible to participate in an attractive bonus plan.
Peraton Overview
Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy.
As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies.
Peraton operates at the critical nexus between traditional and nontraditional threats across all domains : land, sea, space, air, and cyberspace.
The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces.
Each day, our employees do the can't be done by solving the most daunting challenges facing our customers. Visit peraton.
com to learn how we're keeping people around the world safe and secure.
Target Salary Range
$135,000 - $216,000. This represents the typical salary range for this position based on experience and other factors.
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