In this role, you will help integrate AI-enabled capabilities into analyst workflows and mission systems to accelerate cyber data triage, improve decision support, and enhance operational effectiveness.  This role is ideal for a technically strong engineer who understands how to stand up, assess, and guide modern AI platforms in secure government settings, with familiarity in cloud and enterprise ecosystems such as Microsoft Azure and Amazon Web Services (AWS).

What You’ll Be Doing:

• Develop full stack applications and services that leverage large language models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and other emerging AI capabilities.

• Designing, developing, and deploying AI/ML models and tools for data mining, predictive modeling, and knowledge discovery

• Integrate AI/ML-enabled capabilities into a secure, cloud-based data architecture, ensuring interoperability with existing systems via standardized APIs

• Implement and monitor model performance, data/concept drift, and key metrics in real time; support model retraining and fine-tuning as new data becomes available

• Collaborate with data engineers, software developers, and stakeholders to define requirements, validate outputs, and support mission deliverables

• Ensure all AI/ML workflows comply with DoW cybersecurity, data privacy, and access control requirements, including operation within isolated cloud environments.

• Assist in knowledge transfer and training activities, including the development of tutorials and best practices for AI/ML usage.

• Educate stakeholders on AI platform concepts, emerging patterns, and implementation best practices in government environments.

• Assessing data patterns in systems and network misconfiguration.

• Working with a team of Cyber Analysts to support quick turn requests for vulnerability information by continually improving LLMs

• Conduct analyses of alternatives (AoAs) to compare internally developed approaches against commercial, open-source, and industry-standard techniques.

• Develop and execute experiments to assess model performance using appropriate evaluation metrics and representative datasets.

What Required Skills You'll Bring:

• Active TS/SCI with Poly required

• Bachelor of Science in Computer Science, Cybersecurity, Information Systems, or related field and 10+ years of relevant experience. With additional years of experience able to count in lieu of a degree, and vice versa.

• Familiarity with machine learning concepts, including model training, evaluation, tuning, and performance measurement.

• Ability to assess model effectiveness using quantitative metrics and communicate tradeoffs.

• Depth of experience with prompt engineering.

• Proficiency in Python and experience developing RESTful APIs and backend services.

• Experience supporting AI/ML, data, cloud, or platform engineering efforts in government, defense, or enterprise environments.

• Understanding of modern AI concepts, including LLMs, embeddings, RAG, vector search, and agentic workflows.

• Demonstrated familiarity with Microsoft and/or AWS platform ecosystems

• Proficiency using command-line interfaces such as Windows PowerShell/Command Prompt for routine development, troubleshooting, scripting, and automation tasks

• Working knowledge of cloud architecture, containers, APIs, data services, identity/access management, and secure system integration.

• Ability to understand how AI applications depend on underlying platform components such as model hosting, pipelines, storage, orchestration, and monitoring.

• Demonstrated experience in database management, including designing and maintaining relational databases, writing and optimizing SQL queries, implementing backup and recovery procedures, and applying database security and performance best practices.

• Demonstrated experience with configuration management, including defining and maintaining configuration baselines, using configuration management tools e.g.,Ansible, Puppet, Chef, Terraform to manage infrastructure and application settings

• Passionate about AI and platform modernization.

• Ability to rapidly prototype solutions, iterate based on user feedback, and operate effectively in ambiguous problem spaces.

• Able to translate complex technical issues into actionable guidance for mixed technical and non-technical audiences.

• Mission-oriented, collaborative, and proactive.