Senior Machine Learning Engineer

at  Leidos

Description
Looking for the opportunity to work on problems that matter, with colleagues that share your interest and expertise in applied Artificial Intelligence and Machine Learning?
Leidos is looking for a Senior Machine Learning Engineer to apply their expertise in Artificial Intelligence, Machine Learning, and MLOps to develop repeatable workflows that build, train, test, deploy, and monitor trustworthy AI capabilities. The Machine Learning Engineer will create solutions for internal and corporate research and client’s operational environments. This role requires a strong foundation in Machine Learning, experience with DevOps/MLOps tools and processes, Python programming experience, and the ability to work in fast-paced, Agile development teams.
To be successful in this role, you should be highly motivated and collaborative, working well independently and within a team of junior and senior engineers & researchers. You should be effective and documenting your work and comfortable creating and communicating R&D plans, progress, and results.

BASIC QUALIFICATIONS

• Bachelor’s degree with 8 years of experience or Master’s degree with 6 years of experience in Computer Science, Machine Learning, Artificial Intelligence, or related discipline
• Practical, hands-on experience with developing machine learning algorithms & models, visualizations, web apps
• Advanced Python programming skills
• Experience with AI/ML tools, such as common python packages (e.g., scikit-learn, TensorFlow, PyTorch) and Jupyter notebooks
• Experience with MLOps tools and frameworks, such as Kubeflow, MLflow, DVC, TensorBoard
• Experience with Software Development tools, including Git, containerization technologies (e.g., Docker), CI/CD frameworks
• Strong communication skills
• Competence in troubleshooting and mitigating issues with prototyped and deployed AI
• Demonstrated ability to orchestrate a ML pipeline

PREFERRED QUALIFICATIONS

• Experience with AI/ML across a broad range of application domains (e.g., NLP, Computer Vision, time series analysis)
• Experience deploying and using AI Explainability and Monitoring tools
• Experience deploying, managing, and using Kubernetes and Kubeflow clusters
• Experience using Infrastructure-as-Code tools (e.g., Terraform, Ansible, CloudFormation)
• Experience deploying, configuring, and managing DevOps tools (e.g., GitLab, Nexus)
• Ability and willingness to obtain a Top Secret security clearance

• Design and implement tools and processes to enable MLOps practices in a scalable cloud infrastructure
• Design, build, train, and evaluate Machine Learning models
• Build repeatable Machine Learning pipelines for model training, evaluation, deployment, and monitoring
• Perform R&D to enable AI Observability
• Design, implement, and manage cloud resources for MLOps infrastructure
• Work in a team of AI/ML researchers and engineers using Agile development processes