| Location | Norfolk, Virginia |
| Website | www.cditsolutions.com |
The AI Engineer will design,
develop, and deploy machine learning, natural language processing, and
generative AI solutions supporting the NMMES program at Naval Sea Systems
Command (NAVSEA) in Norfolk, VA. The role focuses on turning large volumes of
ship maintenance, logistics, and readiness data into predictive insights and
decision-support tools that improve fleet availability, reduce unplanned
maintenance, and accelerate work-package planning.
The engineer will work directly
with data scientists, software engineers, Navy subject-matter experts, and CACI
program leadership to move models from prototype to production within an AWS
GovCloud environment. This position requires a blend of hands-on ML
engineering, MLOps discipline, and comfort operating in a Defense customer
environment governed by DoD security and accreditation processes.
• Design and build supervised, unsupervised, and
generative AI models (including LLM-based RAG pipelines) against Navy
maintenance, supply, and equipment-history datasets.
• Develop end-to-end ML pipelines — data ingestion,
feature engineering, training, evaluation, deployment, and monitoring — using
Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging
Face).
• Implement MLOps practices in AWS GovCloud using
SageMaker, Bedrock, Step Functions, Lambda, and containerized workloads
(ECS/EKS).
• Apply NLP techniques (entity extraction,
classification, summarization, semantic search) to unstructured maintenance
narratives, casualty reports (CASREPs), and 3M records.
• Collaborate with data engineers to define schemas,
feature stores, and vector databases (OpenSearch, pgvector) that support
production inference.
• Establish model governance practices: version control
for models and datasets, bias and drift monitoring, evaluation harnesses, and
human-in-the-loop feedback loops.
• Document model design, assumptions, and limitations in
a manner suitable for Government review, accreditation, and technical exchange
meetings.
• Support proposal, demonstration, and pilot activities
as directed by CACI and CDIT Solutions leadership.
• 5+ years of hands-on experience building and deploying
ML or AI systems in production.
• Expert-level Python, including data-science tooling
(pandas, NumPy, scikit-learn) and at least one deep-learning framework (PyTorch
or TensorFlow).
• Demonstrated experience with LLMs, prompt engineering,
retrieval-augmented generation (RAG), embeddings, and vector search.
• Working knowledge of AWS ML services — SageMaker,
Bedrock, Lambda, S3, and IAM — preferably in GovCloud (US).
• Experience deploying containerized workloads (Docker,
ECS, or EKS) and building CI/CD pipelines for ML.
• Solid grounding in statistics, model evaluation, and
experimentation methodology.
• Ability to communicate technical concepts clearly to
non-technical Navy and program stakeholders.
• Active DoD Secret clearance at time of hire.
• Prior experience supporting Navy, NAVSEA, or other DoD
maintenance / logistics programs.
• Familiarity with Navy data sources such as NMMES-TR,
Maintenance Figure of Merit (MFOM), OARS, or 3M/MDS.
• Experience with responsible-AI frameworks, model cards,
and DoD AI ethics principles.
• Exposure to knowledge graphs, ontologies, or
graph-based retrieval.
• TS/SCI clearance.
Bachelor’s degree in Computer
Science, Data Science, Applied Mathematics, Statistics, or a related technical
discipline. Master’s or PhD strongly preferred. Additional relevant experience
may be substituted for degree requirements consistent with contract
labor-category definitions.
• DoD 8570 / 8140 IAT Level II baseline certification
(e.g., Security+ CE) — required within 6 months of hire if not currently held.
• AWS Certified Machine Learning – Specialty
• AWS Certified Solutions Architect – Associate or
Professional
• Certified Ethical Hacker (CEH) or CISSP