AI Engineer

Job Description
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.

Key Responsibilities
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.

Required Qualifications
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.

Preferred Qualifications
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.

Education

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.

Certifications
Required
DoD 8570 / 8140 IAT Level II baseline certification (e.g., Security+ CE) - required within 6 months of hire if not currently held.

Preferred
AWS Certified Machine Learning - Specialty
AWS Certified Solutions Architect - Associate or Professional
Certified Ethical Hacker (CEH) or CISSP