As the Machine Learning Engineering Team Lead you will lead the data science and software engineering required to put machine learning (ML) models into production, delivering on technical performance specifications and mission value. Collaborate with Task 2 Experiments Lead to create workflows enabling seamless transition of ML models, proven in experiments, to the incubation and scaling to production phase. Promote emerging best practices in ML engineering including automated tool selection, collaborating with other systems engineering and integration and operations and maintenance leads. Recruit and develop the community of ML engineers on the program. Manage technical staff and technical resources to priority needs. Lead professional development and work culture for high performing teams.
Lead the development and overall vision and direction of machine learning team project preparing and documenting plans for migrating data, managing personnel, and system operation from the existing, legacy systems to a new environment (e.g., new system, C2S, etc.) .
Oversee all ML- related product development efforts, working with operations, engineering, and artificial intelligence leads and customer stakeholders.
Design and develop machine learning applications with varying degrees of scale, risk and complexity.
Serve as the machine learning technical lead for the design, testing, and implementation of complex machine learning applications, complex web application layouts, content and user interfaces and/or database projects.
Run ML projects/initiative, build MVPs, prototypes, from beginning to end
Prototype and demonstrate AI/ML related products and solutions for client stakeholders and key personnel.
Oversee and integrate all machine learning product development efforts, working with all project key personnel and customers stakeholders, and providing leadership within the project machine learning domain.
Research, design, develop, and/or modify enterprise-wide systems and/or applications software.
Provide support to integrated native cloud infrastructure with multi-domain C2S and IC Cloud
Define and deliver on building blocks related to AI/ML capabilities of large enterprise cloud CSP environments.
Play a key role in deciding technologies and related roadmap for development, testing, maintenance and deployment of AI/ machine learning models.
Machine learning or relevant years of experience or expertise
Experience in AWS, Azure or other Cloud Service provider
Experience with DevOps
Experience working in the Agile Software Development and Testing framework
Desirable Skills / Experience:
Experience with CI/CD pipeline
Experience with DevSecOps methods in support of continuous development, continuous integration environments.
Knowledge and understanding of ITIL Service Design process and procedures.
Candidates must be willing and able to attain a CI Polygraph
We are GDIT. The people supporting some of the most complex government, defense, and intelligence projects across the country. We deliver. Bringing the expertise needed to understand and advance critical missions. We transform. Shifting the ways clients invest in, integrate, and innovate technology solutions. We ensure today is safe and tomorrow is smarter. We are there. On the ground, beside our clients, in the lab, and everywhere in between. Offering the technology transformations, strategy, and mission services needed to get the job done.
GDIT is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status, or any other protected class.