33 applicants

Company

Original Job Post

ManTech · 1 week ago

AI/Machine Learning Engineer

USA-VA-Herndon

Full-time

Hybrid

Senior Level

5+ years exp

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Responsibilities

Develop, manage, and maintain Machine Learning infrastructure and environments.

Develop ML models in cloud environments, such as AWS, Azure, and Google Cloud Platform.

Collaborate with data scientists on using data science and machine learning algorithms in areas such as natural language processing, graph analytics, computer vision, and anomaly detection, in accordance with industry and academic best practices.

Deploy, operationalize, and scale analytic and machine learning models and solutions based on those models.

Implement full life cycle of Machine Learning Operations (MLOps), collaborating with data scientists, data engineers, AI application developers, architects, security experts, and others as needed.

Support the development of enterprise strategy and guidance for deployment of ML capabilities across the enterprise.

Maintain leading edge processes as technology and mission needs change.

Analyze current data sources and techniques and apply innovative and big data-driven problem-solving skills to analytic elements.

Provide services related to data visualization and communication of complex quantitative concepts to non-technical audiences.

Apply design thinking or other solution-based methods to solve problems consisting of various qualitative activities that support the generation of insightful, human-centered and impactful design solutions.

Perform knowledge elicitation from client of business problem details and incorporate that knowledge into algorithms, models, and approaches in client solutions.

Identify AI and data science challenges in non-AI projects to enhance the projects’ capabilities.

Translate machine learning related technical requirements to agile tasks prioritized according to the goals and requirements of the client.

Perform solution development through whiteboarding sessions with clients, partners, and coworkers.

Contribute to solutions with machine learning techniques, MLOps practices, architecture, or other technical challenges related to machine learning engineering.

Communicate findings to diverse technical and non-technical stakeholders and engage client to distill complex technical language.

Qualification

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Cloud ProvidersMLOpsMachine LearningAIModel DeploymentFull Life Cycle DevelopmentCommunicationData AnalysisCloud PlatformsProblem-SolvingInitiative

Required

Bachelor’s degree in computer science, Engineering, or Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, or related field and 5 years of experience OR a Master's degree and 3 years of experience or equivalent experience

In depth knowledge with one or more cloud providers (e.g., AWS, Oracle, IBM, Azure and GCP) and experience working across environments and on premise

Strong experience with Machine Learning Operations (MLOps)

Strong experience developing and maintaining cloud environments for machine learning solutions

Strong experience deploying and operationalizing machine learning models and solutions based on those models

Experience engineering and deploying solutions using both conventional AI and generative AI

Experience in the full life cycle of developing a variety of types of Machine Learning models

Strong oral and written communication skills, including client-facing discussions and presentations