Job description

Required Skills: • A master’s degree or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field, along with demonstrated experience working on or developing AI/ML capabilities. • At least four years of relevant AI/ML experience that includes work with large language models, retrieval-augmented generation, and prompt engineering. • Hands-on experience integrating LLM-enabled software and RAG capabilities into applications or workflows. • Developing or supporting agentic-AI capabilities and multi-step AI workflows. • Designing, building, or supporting inference pipelines. • Ability to evaluate AI-enabled capabilities and clearly document findings, design decisions, and results. • Testing and documenting AI-enabled software capabilities. • Ability to clearly explain your personal technical ownership and contributions. • Strong collaboration and technical-communication skills. Preferred Background Experience with several of the following can strengthen your fit: • Moving AI capabilities beyond coursework, personal projects, or demonstrations into operational software workflows. • Evaluating grounding, reliability, output quality, hallucinations, or other limitations of AI-enabled systems. • Integrating AI services with backend APIs or established software applications. • Secure software-development lifecycle and DevSecOps practices. • OpenShift, Kubernetes, CI/CD, or containerized application delivery. • Secure, restricted, disconnected, on-premises, or classified development environments. • Defense, government, aerospace, mission-planning, or other regulated environments. • Collaboration with software-engineering, cybersecurity, platform, and customer-facing technical teams.

Requirements

Must-haves

Required Skills: • A master’s degree or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field, along with demonstrated experience working on or developing AI/ML capabilities.
At least four years of relevant AI/ML experience that includes work with large language models, retrieval-augmented generation, and prompt engineering.
Hands-on experience integrating LLM-enabled software and RAG capabilities into applications or workflows.
Developing or supporting agentic-AI capabilities and multi-step AI workflows.
Designing, building, or supporting inference pipelines.
Ability to evaluate AI-enabled capabilities and clearly document findings, design decisions, and results.
Testing and documenting AI-enabled software capabilities.
Ability to clearly explain your personal technical ownership and contributions.
Strong collaboration and technical-communication skills.
Experience with several of the following can strengthen your fit:
Moving AI capabilities beyond coursework, personal projects, or demonstrations into operational software workflows.
Evaluating grounding, reliability, output quality, hallucinations, or other limitations of AI-enabled systems.
Integrating AI services with backend APIs or established software applications.
Secure software-development lifecycle and DevSecOps practices.
OpenShift, Kubernetes, CI/CD, or containerized application delivery.
Secure, restricted, disconnected, on-premises, or classified development environments.
Defense, government, aerospace, mission-planning, or other regulated environments.
Collaboration with software-engineering, cybersecurity, platform, and customer-facing technical teams.

Nice-to-haves

Preferred Background

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlllmkubernetesci/cd

Platform: jazzhr
Effort: medium