Data Scientist / AI Engineer
$155,000–$165,000 year
HybridBremerton, Washington, United States or Norfolk, Virginia, United States
Bremerton, Washington, United States or Norfolk, Virginia, United StatesHybridFull Time$155,000–$165,000 yearSenior LevelDoctorate Or Professional DegreeSmall
Full TimeSenior LevelDoctorate Or Professional DegreeSmall
Job Summary
Design and deploy production-grade Retrieval-Augmented Generation (RAG) systems, agentic AI workflows, and LLM-powered analytics tailored to carrier hotwashes and project performance data. Architect scalable RAG pipelines, multi-agent systems, and data ingestion strategies using Databricks, AWS SageMaker, and LangGraph to transform unstructured naval maintenance data into actionable insights. Partner with analysts and subject-matter experts to build AI-enhanced features for the Operations Dashboard, including natural language querying, automated hotwash summarization, and risk forecasting. Establish MLOps practices, evaluation harnesses, and responsible AI governance aligned with DoD standards while mentoring junior professionals and conducting technology scanning for emerging capabilities.
Required Qualifications
- 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
- Ability to rapidly acquire and apply context from complex naval maintenance, engineering, logistics, and project management domains
- 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
- Active or recent Secret (or higher) security clearance
- U.S. Citizenship
- Regular telework or hybrid arrangement (typically 2–3 days per week on-site)
Desired Qualifications
- Prior DoD/Navy/shipyard or heavy industrial experience
- 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 environments
- Master's degree or higher from an accredited institution in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, Operations Research, or a closely related quantitative field
- Ph.D. is advantageous for roles with significant research/prototyping elements
- Generally 4–6+ years of professional experience, with stronger emphasis on independent ownership of production or near-production RAG/agentic LLM systems, deeper technical leadership, and the ability to operate with minimal supervision on complex, high-stakes problems from day one
- Generally 2–4 years of professional experience in data science, machine learning engineering, or AI application development
- Clear, meaningful contribution to LLM, RAG, or other complex ML/AI systems (production, near-production, or high-impact pilot systems that delivered measurable value)
- Exceptional learning agility, intellectual curiosity, and drive
- Outstanding portfolios or rapid progression on complex technical projects
- Meaningful personal contribution to the design, implementation, significant improvement, or successful adoption of RAG, agentic LLM, or other complex ML/AI systems applied to technical or operational use cases
- Evidence of rapid learning, high-quality delivery under ambiguity, intellectual curiosity, and measurable impact
- Equivalent experience on Azure ML or Google Vertex AI
- Experience with LlamaIndex + custom orchestration
- Experience with Hugging Face Transformers
- Experience with Pandas/Polars
- Experience with Spark/Databricks Delta Lake
- Experience with vector databases
- Experience with embedding models
- Experience with prompt engineering
- Experience with evaluation frameworks
- Experience with grounding/citation techniques
- Experience with LangChain/LangGraph
- Experience with CI/CD
- Experience with monitoring
- Experience with versioning
- Experience with NLP techniques for technical and semi-structured text (chunking, entity extraction, summarization, semantic search)
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience leading requirements workshops
- Experience conducting validation sessions
- Experience conducting iterative co-design with domain experts
- Experience producing clear technical documentation
- Experience briefing technical and non-technical audiences up to senior executive/flag level
- Experience communicating capabilities, trade-offs, risks, and measured outcomes
- Experience collaborating with domain experts who may have limited AI background
- Experience operating in a fast-paced operational environment with competing priorities and evolving requirements
- Experience with AWS SageMaker
- Experience with Databricks
- Experience with Azure ML
- Experience with Google Vertex AI
- Experience building production data pipelines
- Experience managing the full lifecycle of AI solutions from prototype through sustained operations with measurable SLAs
- Experience with NLP techniques for technical and semi-structured text
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience with knowledge graphs
- Experience with hybrid search
- Experience with multi-modal models
- Experience with LLM fine-tuning (parameter-efficient or continued pre-training) in regulated environments
- Experience with AWS Certified Machine Learning – Specialty
- Experience with relevant LLMOps or MLOps certifications
- Experience shipping production AI features that delivered quantified operational or business impact in complex environments
- Master's degree or higher from an accredited institution in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, Operations Research, or a closely related quantitative field
- Ph.D.
- Generally 4–6+ years of professional experience
- Generally 2–4 years of professional experience in data science, machine learning engineering, or AI application development
- Clear, meaningful contribution to LLM, RAG, or other complex ML/AI systems
- Exceptional learning agility, intellectual curiosity, and drive
- Outstanding portfolios or rapid progression on complex technical projects
- Meaningful personal contribution to the design, implementation, significant improvement, or successful adoption of RAG, agentic LLM, or other complex ML/AI systems applied to technical or operational use cases
- Evidence of rapid learning, high-quality delivery under ambiguity, intellectual curiosity, and measurable impact
- Experience with LlamaIndex
- Experience with custom orchestration
- Experience with Hugging Face Transformers
- Experience with Pandas
- Experience with Polars
- Experience with SQL
- Experience with Spark
- Experience with Databricks Delta Lake
- Experience with vector databases
- Experience with embedding models
- Experience with prompt engineering
- Experience with evaluation frameworks
- Experience with grounding/citation techniques
- Experience with LangChain
- Experience with LangGraph
- Experience with CI/CD
- Experience with monitoring
- Experience with versioning
- Experience with NLP techniques for technical and semi-structured text (chunking, entity extraction, summarization, semantic search)
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience leading requirements workshops
- Experience conducting validation sessions
- Experience conducting iterative co-design with domain experts
- Experience producing clear technical documentation
- Experience briefing technical and non-technical audiences up to senior executive/flag level
- Experience communicating capabilities, trade-offs, risks, and measured outcomes
- Experience collaborating with domain experts who may have limited AI background
- Experience operating in a fast-paced operational environment with competing priorities and evolving requirements
- Experience with AWS SageMaker
- Experience with Databricks
- Experience with Azure ML
- Experience with Google Vertex AI
- Experience building production data pipelines
- Experience managing the full lifecycle of AI solutions from prototype through sustained operations with measurable SLAs
- Experience with NLP techniques for technical and semi-structured text
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience with knowledge graphs
- Experience with hybrid search
- Experience with multi-modal models
- Experience with LLM fine-tuning (parameter-efficient or continued pre-training) in regulated environments
- Experience with AWS Certified Machine Learning – Specialty
- Experience with relevant LLMOps or MLOps certifications
- Experience shipping production AI features that delivered quantified operational or business impact in complex environments
- Master's degree or higher from an accredited institution in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, Operations Research, or a closely related quantitative field
- Ph.D.
- Generally 4–6+ years of professional experience
- Generally 2–4 years of professional experience in data science, machine learning engineering, or AI application development
- Clear, meaningful contribution to LLM, RAG, or other complex ML/AI systems
- Exceptional learning agility, intellectual curiosity, and drive
- Outstanding portfolios or rapid progression on complex technical projects
- Meaningful personal contribution to the design, implementation, significant improvement, or successful adoption of RAG, agentic LLM, or other complex ML/AI systems applied to technical or operational use cases
- Evidence of rapid learning, high-quality delivery under ambiguity, intellectual curiosity, and measurable impact
- Experience with LlamaIndex
- Experience with custom orchestration
- Experience with Hugging Face Transformers
- Experience with Pandas
- Experience with Polars
- Experience with SQL
- Experience with Spark
- Experience with Databricks Delta Lake
- Experience with vector databases
- Experience with embedding models
- Experience with prompt engineering
- Experience with evaluation frameworks
- Experience with grounding/citation techniques
- Experience with LangChain
- Experience with LangGraph
- Experience with CI/CD
- Experience with monitoring
- Experience with versioning
- Experience with NLP techniques for technical and semi-structured text (chunking, entity extraction, summarization, semantic search)
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience leading requirements workshops
- Experience conducting validation sessions
- Experience conducting iterative co-design with domain experts
- Experience producing clear technical documentation
- Experience briefing technical and non-technical audiences up to senior executive/flag level
- Experience communicating capabilities, trade-offs, risks, and measured outcomes
- Experience collaborating with domain experts who may have limited AI background
- Experience operating in a fast-paced operational environment with competing priorities and evolving requirements
- Experience with AWS SageMaker
- Experience with Databricks
- Experience with Azure ML
- Experience with Google Vertex AI
- Experience building production data pipelines
- Experience managing the full lifecycle of AI solutions from prototype through sustained operations with measurable SLAs
- Experience with NLP techniques for technical and semi-structured text
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience with knowledge graphs
- Experience with hybrid search
- Experience with multi-modal models
- Experience with LLM fine-tuning (parameter-efficient or continued pre-training) in regulated environments
- Experience with AWS Certified Machine Learning – Specialty
- Experience with relevant LLMOps or MLOps certifications
- Experience shipping production AI features that delivered quantified operational or business impact in complex environments
- Master's degree or higher from an accredited institution in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, Operations Research, or a closely related quantitative field
- Ph.D.
- Generally 4–6+ years of professional experience
- Generally 2–4 years of professional experience in data science, machine learning engineering, or AI application development
- Clear, meaningful contribution to LLM, RAG, or other complex ML/AI systems
- Exceptional learning agility, intellectual curiosity, and drive
- Outstanding portfolios or rapid progression on complex technical projects
- Meaningful personal contribution to the design, implementation, significant improvement, or successful adoption of RAG, agentic LLM, or other complex ML/AI systems applied to technical or operational use cases
- Evidence of rapid learning, high-quality delivery under ambiguity, intellectual curiosity, and measurable impact
- Experience with LlamaIndex
- Experience with custom orchestration
- Experience with Hugging Face Transformers
- Experience with Pandas
- Experience with Polars
- Experience with SQL
- Experience with Spark
- Experience with Databricks Delta Lake
- Experience with vector databases
- Experience with embedding models
- Experience with prompt engineering
- Experience with evaluation frameworks
- Experience with grounding/citation techniques
- Experience with LangChain
- Experience with LangGraph
- Experience with CI/CD
- Experience with monitoring
- Experience with versioning
- Experience with NLP techniques for technical and semi-structured text (chunking, entity extraction, summarization, semantic search)
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience leading requirements workshops
- Experience conducting validation sessions
- Experience conducting iterative co-design with domain experts
- Experience producing clear technical documentation
- Experience briefing technical and non-technical audiences up to senior executive/flag level
- Experience communicating capabilities, trade-offs, risks, and measured outcomes
- Experience collaborating with domain experts who may have limited AI background
- Experience operating in a fast-paced operational environment with competing priorities and evolving requirements
- Experience with AWS SageMaker
- Experience with Databricks
- Experience with Azure ML
- Experience with Google Vertex AI
- Experience building production data pipelines
- Experience managing the full lifecycle of AI solutions from prototype through sustained operations with measurable SLAs
- Experience with NLP techniques for technical and semi-structured text
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience with knowledge graphs
- Experience with hybrid search
- Experience with multi-modal models
- Experience with LLM fine-tuning (parameter-efficient or continued pre-training) in regulated environments
- Experience with AWS Certified Machine Learning – Specialty
- Experience with relevant LLMOps or MLOps certifications
- Experience shipping production AI features that delivered quantified operational or business impact in complex environments
- Master's degree or higher from an accredited institution in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, Operations Research, or a closely related quantitative field
- Ph.D.
- Generally 4–6+ years of professional experience
- Generally 2–4 years of professional experience in data science, machine learning engineering, or AI application development
- Clear, meaningful contribution to LLM, RAG, or other complex ML/AI systems
- Exceptional learning agility, intellectual curiosity, and drive
- Outstanding portfolios or rapid progression on complex technical projects
- Meaningful personal contribution to the design, implementation, significant improvement, or successful adoption of RAG, agentic LLM, or other complex ML/AI systems applied to technical or operational use cases
- Evidence of rapid learning, high-quality delivery under ambiguity, intellectual curiosity, and measurable impact
- Experience with LlamaIndex
- Experience with custom orchestration
- Experience with Hugging Face Transformers
- Experience with Pandas
- Experience with Polars
- Experience with SQL
- Experience with Spark
- Experience with Databricks Delta Lake
- Experience with vector databases
- Experience with embedding models
- Experience with prompt engineering
- Experience with evaluation frameworks
- Experience with grounding/citation techniques
- Experience with LangChain
- Experience with LangGraph
- Experience with CI/CD
- Experience with monitoring
- Experience with versioning
- Experience with NLP techniques for technical and semi-structured text (chunking, entity extraction, summarization, semantic search)
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience leading requirements workshops
- Experience conducting validation sessions
- Experience conducting iterative co-design with domain experts
- Experience producing clear technical documentation
- Experience briefing technical and non-technical audiences up to senior executive/flag level
- Experience communicating capabilities, trade-offs, risks, and measured outcomes
- Experience collaborating with domain experts who may have limited AI background
- Experience operating in a fast-paced operational environment with competing priorities and evolving requirements
- Experience with AWS SageMaker
- Experience with Databricks
- Experience with Azure ML
- Experience with Google Vertex AI
- Experience building production data pipelines
- Experience managing the full lifecycle of AI solutions from prototype through sustained operations with measurable SLAs
- Experience with NLP techniques for technical and semi-structured text
- Experience applying NLP techniques to real-world operational or maintenance datasets
- Experience with knowledge graphs
- Experience with hybrid search
- Experience with multi-modal models
- Experience with LLM fine-tuning (parameter-efficient or continued pre-training) in regulated environments
- Experience with AWS Certified Machine Learning – Specialty
- Experience with relevant LLMOps or MLOps certifications
- Experience shipping production AI features that delivered quantified operational or business impact in complex environments
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