AI Engineer Associate

Description

SAIC is� seeking a machine learning engineer with hands-on experience deploying machine learning and artificial intelligence models.� Experience with on-premises deployment of large language models (LLMs) is desired. The candidate will join the dynamic team at the Identity and Data Sciences Laboratory (IDSL).� The IDSL is tasked with evaluating how new AI technologies can be best integrated into operational use-cases across the US government.� We are investigating how AI systems can improve process efficiency and effectiveness as well as how to optimally team AI systems with human operators. Our engineers enjoy a great work-life balance and work in an environment that promotes learning and exploration.

The IDSL is based in Upper Marlboro, MD. � Up to 20% travel is expected within the continental US.� This is a hybrid position with 3 days per week expected in the office.

Responsibilities:

• Deploy, configure, and maintain machine learning models and large language models (LLMs) on GPU-enabled on-premises servers and cloud infrastructure, ensuring high availability and reliability for all team stakeholders.
• Design and implement secure, efficient data pipelines for AI systems within air-gapped and networked environments.
• Develop and integrate agentic AI pipelines leveraging open-source LLMs and on-premises GPU hardware.
• Contribute to in-person data collections at the Maryland Test Facility.
• Stay current with the rapidly evolving AI hardware and software landscape, identifying and recommending improvements to tooling, infrastructure, and deployment practices.
• Collaborate frequently with software, networking, data science, and cloud engineering teams to align AI infrastructure with existing laboratory infrastructure.
• Work closely with the lead AI scientist to carry out these responsibilities.

Qualifications

Required:

• BS in computer science, machine learning, computer vision, biometrics or a related field.

• Strong programming skills in languages such as Python and version control systems such as Git.
• Hands-on experience deploying and configuring LLMs, including an understanding of prompt engineering techniques.
• Experience specifying and working with GPU hardware to meet the performance demands of AI workloads.
• Experience with AI frameworks like TensorFlow or PyTorch.

• Experience deploying and configuring LLMs and an understanding of prompt engineering.

• Excellent analytical, communication, and problem-solving skills with the ability to work effectively across multidisciplinary teams.

Desired:

• Familiarity with biometric and identity systems, including face, iris, or fingerprint recognition technologies.
• Familiarity with RAG, Vector Stores, and Agentic AI.
• Familiarity with cloud platforms such as AWS, including managed AI and compute services (e.g., Amazon Bedrock).
• Exposure to secure or air-gapped deployment environments and associated data handling requirements.
• Awareness of current and emerging AI hardware options and a demonstrated habit of tracking developments in the field.
• Experience operating Agentic AI coding tools (e.g., Claude Code) in a safe, structured, and productive manner.