Looking for the opportunity to work on problems that matter, with colleagues that share your interest and expertise in cloud engineering and Artificial Intelligence and Machine Learning integration? 

Leidos is looking for a Senior AI Cloud Engineer to apply their expertise in Cloud Engineering, software packaging, and Artificial Intelligence and Machine Learning, to develop repeatable architectures and integration packages for AI capabilities. The Senior AI Cloud Engineer will support a team of data scientists and engineers to create deployable AI solutions for corporate and client’s operational environments. This role requires a strong foundation in Cloud Engineering, Software Engineering, 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.

Primary Responsibilities

-  Design, deploy, and manage cloud-oriented solutions from major vendors such as Amazon Web Services, Microsoft Azure, Google, Oracle Cloud Compute, and IBM

- Automate cloud deployments using Ansible and/or TerraForm

- Provide technical expertise and assist with systems integration and consolidation efforts across multiple systems environments

- Interface with customers, vendors and other technical staff to analyze business and technical requirements.

Engineer/implement technical solutions from a cradle to grave technical approach

- Assist/train engineers and Data Scientist on new technologies to improve the organizations technical expertise

- Work in a team of AI/ML researchers and engineers using Agile development processes

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

- Experience with Amazon Web Services (AWS)

- Experience with Microsoft Azure

- Experience with cloud infrastructure instantiation, design, implementation, and management

- Demonstrated experience with containers (i.e. Docker, Kubernetes)

- Practical, hands-on experience with developing solutions for system integration and automation

- Advanced Python programming skills and experience with Python processing tools such as Jupyter notebooks

- Experience with Software Development tools, including Git CI/CD frameworks

- Strong communication skills

- Competence in troubleshooting and mitigating issues with prototyped and deployed AI

- Demonstrated ability to document your work and be comfortable creating and communicating integration plans, progress, and results

Preferred Qualifications

- Bachelor’s degree with 10 years of experience or Master’s degree with 8 years of experience in Computer Science, Machine Learning, Artificial Intelligence, or related discipline

- Experience with AI/ML tools, such as common python packages (e.g., scikit-learn, TensorFlow, PyTorch) and Jupyter notebooks

- Knowledge of AI/ML across a broad range of application domains (e.g., NLP, Computer Vision, time series analysis)

-  Experience with MLOps tools and frameworks, such as Kubeflow, MLflow, DVC, TensorBoard

- Experience deploying, configuring, and managing DevOps tools (e.g., GitLab, Nexus)

-  Demonstrated ability to orchestrate ML pipelines

- Experience in deploying LLMs based solution in either cloud or on-prem

- Experience with code optimization,

- Experience with security protocols

Original Posting Date:

While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.