Data Scientist / AI Engineer

Senior Data Scientist / AI EngineerCarrier Team One (CT1) leads transformative data-driven process improvement and knowledge management initiatives for nuclear aircraft carrier maintenance and modernization. This includes maintenance support for all aircraft carrier availabilities, including Refueling Complex Overhaul (RCOH), Planned Incremental Availability (PIA), Selected Restrictive Availability (SRA), Docking Planned Incremental Availability (DPIA), and Carrier Incremental Availability (CIA). The Senior Data Scientist / AI Engineer serves as the principal technical expert and hands-on builder for CT1's advanced analytics and artificial intelligence capabilities. This role directly supports the mission to accelerate capability delivery to the fleet, reclaim decades of obsolescence across alterations, and optimize the expenditure of thousands of man-days and millions of dollars per availability. CT1 particularly values candidates who combine strong technical capability with exceptional learning agility, intellectual curiosity, and drive to rapidly master complex domains and deliver measurable results. This work builds upon CT1's existing Operations Dashboard and Qlik Sense analytics investments that have already demonstrated $9.3M+ savings and 5,200+ man-day reductions per project.This is a senior technical individual-contributor role (non-supervisory) requiring deep expertise in modern LLM engineering, production MLOps, and the ability to translate operational Navy requirements into reliable, secure, and measurable AI solutions. The position demands both exceptional technical craftsmanship and strong stakeholder communication skills to brief results and limitations to project superintendents, engineers, senior leadership, and external stakeholders (NAVSEA, PEO, TYCOM).The primary focus of this position is the design, development, deployment, and continuous improvement of production-grade Retrieval-Augmented Generation (RAG) systems, agentic AI workflows, and LLM-powered analytics tailored to carrier hotwashes (after-action reviews), project performance data, technical documentation, and operational decision support. The incumbent will leverage and extend platforms including ADVANA, Databricks, AWS SageMaker, LangChain/LangGraph, and cURL to turn complex, unstructured, and structured naval maintenance data into actionable insights, automated summaries, predictive signals, and natural-language query capabilities.Position Requirements:35% — LLM/RAG Pipeline Architecture, Development & ProductionizationLead the end-to-end design and implementation of scalable, secure RAG and multi-agent systems. Select and optimize embedding models, chunking strategies, hybrid retrieval (vector + keyword + metadata), reranking, and context compression techniques specifically tuned for technical naval maintenance documentation, alteration history, hotwash narratives, and project artifacts. Implement hallucination detection, citation/grounding mechanisms, and domain-adapted evaluation metrics. Deploy and iterate on LangChain/LangGraph (or equivalent) orchestration layers integrated with approved LLM endpoints (Gemini primary; others as authorized). Ensure solutions meet performance, cost, latency, and reliability targets for operational use.20% — Data Engineering, Ingestion & Vector InfrastructureArchitect and maintain robust data pipelines that ingest, clean, enrich, version, and serve data from heterogeneous shipyard sources (ADVANA datasets, MAXIMO or equivalent EAM systems, Qlik extracts, project schedules, technical manuals, and unstructured logs). Implement vector stores, metadata filtering, and feature stores on Databricks or SageMaker. Establish data quality monitoring, lineage, and governance aligned with DoD and Navy data standards. Enable both batch and near-real-time capabilities as required for hotwash cycles and availability execution.15% — AI-Augmented Operational Analytics & Decision SupportPartner with CT1 analysts, Work Integration Managers, Assistant Project Superintendents, and Process Masters to identify high-impact AI use cases. Build and productionize AI-enhanced features for the CT1 Operations Dashboard and related tools: natural language querying of project data, automated hotwash summarization and insight extraction, risk flagging, duration/resource forecasting, and semantic search over historical alterations and lessons learned. Quantify and communicate ROI in terms of man-days saved, cost avoidance, schedule compression, and improved decision quality.10% — MLOps, Evaluation, Monitoring & Responsible AIEstablish production MLOps practices: prompt/model versioning, CI/CD for pipelines and agents, automated regression testing, drift detection, cost tracking, and observability. Design and maintain rigorous, Navy-context-specific evaluation harnesses (offline benchmarks + online A/B or human feedback loops). Champion and implement DoD AI ethical principles, bias auditing, transparency, human-in-the-loop safeguards, and compliance with emerging Navy/DoD AI governance and cybersecurity requirements (including RMF/ATO considerations for any new capabilities).10% — Cross-Functional Collaboration, Validation & Knowledge TransferServe as the primary AI technical liaison to CT1's cross-functional teams and the broader Knowledge Management Community of Practice (KM COP). Conduct requirements workshops, demo iterations, and validation sessions with subject-matter experts (welders, planners, engineers, logisticians). Translate complex technical concepts and model limitations into plain language for senior decision-makers. Document architectures, runbooks, prompt libraries, and lessons learned. Actively contribute to CT1's knowledge management, process improvement, and innovation initiatives, including agentic research efforts.5% — Research, Prototyping & Technology ScanningContinuously scan the rapidly evolving LLM/agent/RAG landscape for high-value, low-risk capabilities that can be adopted within approved cloud and security boundaries. Rapidly prototype promising approaches against real CT1 use cases (e.g., multi-agent hotwash analysis, knowledge graph augmentation of RAG, predictive signals from unstructured maintenance text). Provide concise technology assessments and recommendations to leadership.5% — Mentorship, Documentation, Compliance & Continuous ImprovementMentor junior data professionals, contractors, or rotating personnel on best practices. Maintain living technical documentation and contribute to CT1's knowledge base. Support audits, data calls, and continuous monitoring requirements. Identify process or tooling improvements that increase team velocity and solution quality. Perform other related duties as assigned in support of CT1 mission objectives.General Experience:Required Technical Competencies• Expert-level knowledge and hands-on production experience with modern LLM engineering, RAG architectures, agentic workflows (LangGraph or strong equivalent), prompt engineering, evaluation frameworks, and grounding/citation techniques.• Advanced proficiency in Python and the LLM/data ecosystem: LangChain/LangGraph (or LlamaIndex + custom orchestration), vector databases, embedding models, Hugging Face Transformers (as needed), Pandas/Polars, SQL, and Spark/Databricks Delta Lake.• Strong practical experience deploying and operating ML/AI workloads on cloud platforms, with preference for AWS SageMaker and/or Databricks; equivalent experience on Azure ML or Google Vertex AI is highly transferable.• Demonstrated ability to build production data pipelines, implement MLOps (CI/CD, monitoring, versioning), and manage the full lifecycle of AI solutions from prototype through sustained operations with measurable SLAs.• Solid understanding of NLP techniques for technical and semi-structured text (chunking, entity extraction, summarization, semantic search) and experience applying them to real-world operational or maintenance datasets.Required Domain & Soft Competencies• Ability to rapidly acquire and apply context from complex naval maintenance, engineering, logistics, and project management domains; prior DoD/Navy/shipyard or heavy industrial experience is a strong plus but not mandatory if accompanied by proven ability to learn technical domains quickly.• Excellent written and oral communication skills, including the ability to produce clear technical documentation and to brief technical and non-technical audiences up to senior executive/flag level on capabilities, trade-offs, risks, and measured outcomes.• Strong collaboration and facilitation skills; comfortable leading requirements workshops, validation sessions, and iterative co-design with domain experts who may have limited AI background.• High degree of self-motivation, intellectual curiosity, and disciplined execution in a fast-paced operational environment with competing priorities and evolving requirements.Preferred / Highly Desirable• Active or recent Secret (or higher) security clearance.• Prior experience supporting Navy, NAVSEA, shipyard, or other DoD maintenance/modernization analytics or AI initiatives.• Hands-on familiarity with ADVANA, Databricks Unity Catalog, or Navy/DoD data platforms and governance frameworks.• Experience with knowledge graphs, hybrid search, multi-modal models, or LLM fine-tuning (parameter-efficient or continued pre-training) in regulated environments.• AWS Certified Machine Learning – Specialty or equivalent cloud ML certification; relevant LLMOps or MLOps certifications.• Track record of shipping production AI features that delivered quantified operational or business impact in complex environmentsAdditional Requirements:EducationMaster's degree or higher from an accredited institution in Data Science, Computer