Cloud Engineer/Machine Learning
Washington, DC - hybrid
TS/SCI clearance required

As a Cloud Engineer with Machine Learning experience, you will?be responsible for developing and deploying machine learning models to solve complex problems across a cloud?infrastructure that houses sensitive data and models that support mission critical operations. You will work closely and collaborate with a dynamic cross-functional team crafting data pipelines for model training to implement machine learning algorithms.

Qualifications:

• TS-SCI clearance?

• Bachelor's degree or equivalent practical experience.?

• 8+ years of experience in computer science, data science, machine learning

• Proven experience developing and deploying machine learning models in a production environment.

• Hands-on experience with major cloud platforms?

• Strong proficiency in programming languages such as Python, along with libraries like Tensorflow, PyTorch, or scikit-learn.

• Understanding of version control, testing, and debugging.

• Strong communications skills with the ability to effectively convey technical concepts to various stakeholders and audiences

• Agile development experience along with related technologies (e.g., Jira)??

Preferred Qualifications:

• Deep knowledge of security best practices, experience with cloud security tools, and working with role and attribute based security models?

• Deep knowledge of DevSecOps and MLOPs

• Familiarity and experience with the Intelligence Community (IC), and the intel cycle.?

• Familiarity and experience with the Department of Homeland Security (DHS).?

• Minimum of Bachelor's Degree, Master's Degree preferred.?

Required professional and vendor certifications:

AWS Certified Developer Associate

Responsibilities:

• Develop and deploy machine learning models to solve complex problems related to various mission focus areas

• Leverage various cloud platforms such as AWS, Azure, or Google cloud to build, train, and deploy machine learning models at scale

• Collaborate with team members to establish data pipelines for model training and inference

• Implement and optimize machine learning algorithms for efficiency and performance

• Conduct thorough experimentation and analysis to improve model accuracy and performance

• Participate in informal and formal code reviews

• Participate in architectural reviews and offer alternative architectural patterns to optimize application performance?

• Document processes and solutions?that serve as communication findings for both technical and non-technical stakeholder

Skills:

• GitLab

• Python and R Studio

• Cloud Services (AWS, GCP, Oracle)

• Natural Language Processing (NLP)

• Data Science

• Machine Learning

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