Booz Allen
intelligence community
MLOpsEngineer,Mid
Neural analysis suggests this role is
optimal for Mid candidates.
“MLOps Engineer, Mid at Booz Allen. Skills: MLOps, Cloud, Python, Machine Learning. design and architect an MLOps platform. shorten time to production”
What You'll Achieve.
shortens the time it takes to get new capabilities from development to production; improve national security
Industry & Context.
help your customers meet their toughest challenges
TS/SCI clearance with a polygraph, Ability to obtain a Security+ CE, SSCP, CCNA-Security, or GSEC certification within 6 months of hire date, security investigation, eligibility requirements for access to classified TS/SCI clearance with polygraph
What They're Looking For.
Must Have
Object-Oriented Programming (OOP), Python, developing containerized applications, API design, authentication, developing software using cloud technologies, AWS, leveraging MLOps platforms, Machine Learning (ML) CI/CD workflows, manage datasets, model training, deployment, monitoring, ML lifecycle, ML concepts, develop an MLOps ecosystem, TS/SCI clearance with a polygraph, HS diploma or GED, Ability to obtain a Security+ CE, SSCP, CCNA-Security, or GSEC certification within 6 months of hire date
Nice to Have
developing prompts, tools, agents with LLMs, AWS SageMaker, Lambda, API Gateway, DynamoDB, S3, IAM, Kubernetes, IaC tools, Helm, Terraform, design and implementation, building, containerizing, deploying end-to-end automated data and ML pipelines, Cloud environment, version control tools, Git, Bachelor’s degree, Master’s degree, Security+ CE, SSCP, CCNA-Security, or GSEC Certification
What You'll Do.
design and architect an MLOps platform
shorten time to production
support mission critical operations
streamline development life cycle
build and maintain Cloud software
support machine learning
implement continuous integration
implement continuous deployment
identify new opportunities
How You'll Work.
Team & Collaboration
working with a collaborative Agile development team
Process & Methodology
Agile development
Full Job Description
MLOps Engineer, Mid **The Opportunity** : Are you looking for an opportunity to make a difference and help build a system that will have a positive impact in the intelligence community (IC)? What if you could find a position that is tailor made for your mix of development, engineering, and analytics? Efficient development teams make the most of their time by limiting the activities that take developers and data scientists away from writing their code. That’s why we need you, an experienced machine learning engineer, to help us design and architect an MLOps platform in the Cloud that shortens the time it takes to get new capabilities from development to production, to support mission critical operations. As an MLOps Engineer on our team, you’ll use your development experience to streamline our development life cycle from development to production. You’ll be working with a collaborative Agile development team to build and maintain Cloud software and infrastructure that supports machine learning across the enterprise. You’ll implement continuous integration and deployment to development, testing, and production environments. This is an opportunity to broaden your skill set into areas like Agile development, Cloud-based development, containerization, and serverless while developing software that will improve national security. As a machine learning engineer, you’ll identify new opportunities to build solutions and architecture to help your customers meet their toughest challenges. Join our team as we build tools to transform the future of the IC. Join us. The world can’t wait. **You Have:** * Experience with Object-Oriented Programming (OOP), including in Python * Experience with developing containerized applications, including API design and authentication * Experience with developing software using cloud technologies, including AWS * Experience with leveraging MLOps platforms and Machine Learning (ML) CI/CD workflows to manage datasets and model training, deployment,
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