Data Scientist / AI Engineer in Chesapeake

Energy Jobline ZR; Chesapeake (VA); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Competitive salaries

Comprehensive medical plans

Company paid insurance (short/long‑trm

Traditional and Roth 401(k) plans

Professional development up to $2,500/

Paid flexible time off

9 paid holidays

Job summary

Phoenix Group of Virginia seeks a Senior Data Scientist / AI Engineer to design, develop and deploy production-grade RAG systems and agentic AI workflows for naval maintenance and modernization programs. You will transform complex maintenance data into actionable insights, summaries, and decision-support analytics.

The role requires deep LLM engineering expertise, strong Python skills, and experience with cloud ML platforms.

Qualifications

• Expert-level LLM engineering, RAG architectures, and agentic workflows.
• Strong Python and LLM/data ecosystem skills including Databricks and cloud ML tools.
• Experience delivering production AI features with measurable impact.

Responsibilities

• Design, develop, deploy, and improve production-grade RAG systems and AI workflows.
• Translate naval maintenance data into actionable insights and predictive signals.
• Collaborate with stakeholders and brief leadership on results and limitations.
• Ensure responsible AI practices and governance in deployments.

Skills

LLM engineering

RAG architectures

agentic workflows

prompt engineering

evaluation frameworks

grounding techniques

Python

cloud ML pipelines

Education

Master’s degree or higher

PhD advantageous

Tools

LangChain

LangGraph

Databricks

AWS SageMaker

cURL

Pandas/Polars

Job description

Job DescriptionJob Description

Position Summary:

Carrier 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.

Position Description:

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).

Primary Functions:

The primary focus of this position is the design, development, deployment, and continuous improvement of production‑grade Retrieval‑Augmented (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‑query capabilities.

Position Requirements:

35% — LLM/RAG Pipeline Architecture, Development & Productionization

20% — Data Engineering, Ingestion & Vector Infrastructure

15% — AI‑Augmented Operational Analytics & Decision Support

10% — MLOps, Evaluation, Monitoring & Responsible AI

10% — Cross‑Functional Collaboration, Validation & Knowledge Transfer

5% — Research, Prototyping & Technology Scanning

5% — Mentorship, Documentation, Compliance & Continuous Improvement

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.

/ 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 environments

Additional Requirements:

Education

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 is strongly . A Ph.D. is advantageous for roles with significant research/prototyping elements but is not required.

Or

Experience

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.

Or

Generally 2–4 years of professional experience in data science, machine learning engineering, or AI application development. Candidates must demonstrate 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 are heavily weighted. Outstanding portfolios or rapid progression on complex technical projects can offset modestly lower years of experience.

All candidates must show, through resume, projects, and interview, 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 will be weighted heavily.

Purely academic, notebook‑only, or low‑impact proof‑of‑concept work without clear stakeholder value or learning agility will generally not meet this factor at either level.

Work Environment and Physical Requirements:

• U.S. Citizenship
• Security Clearance — Secret clearance
• Telework / Hybrid — Regular telework or hybrid arrangement (typically 2–3 days per week on‑site or as mission dictates) is available and encouraged where duties permit. Some work (classified discussions, certain data access, collaboration sessions, shipyard walkthroughs) will require on‑site presence at naval facilities.
• Travel — TDY (estimated 10 – 12 trips per year) to naval shipyards, or conferences for coordination, requirements gathering, training, or knowledge sharing.
• Cybersecurity & AI Governance — Must comply with all applicable DoD, Navy; cybersecurity policies, AI use guidelines, data handling requirements (including CUI and classified information), and RMF/ATO processes for any new capabilities developed or integrated.
• Ethics & Standards — Incumbent is expected to model the highest standards of professional conduct, intellectual honesty, and commitment to responsible, mission‑aligned AI development

Phoenix Group of Virginia Offers:

• Competitive salaries
• Comprehensive medical plans with Employee Assistance Program (EAP), health savings and flexible spending accounts, and dental and vision coverage options
• Company Paid Short‑term and Long‑Term Insurance
• Traditional and Roth 401(K) plans
• Professional development including up to $2,500/year reimbursement for pre‑approved courses, trainings, continued education, and/or certifications
• Paid Flexible Time Off
• 9 paid holidays