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General Dynamics Land Systems is seeking an experienced Generative AI / Enterprise Data Architect to lead the design and implementation of our enterprise data fabric and digital thread. This highly visible role will be a key technical and strategic partner to engineering, IT, operations, and business leaders, enabling an end‑to‑end digital thread that connects contracts through engineering, manufacturing, and supply chain, ultimately into sustainment.

As a Generative AI / Enterprise Data Architect, you will define and implement our Databricks‑based enterprise data architecture, lead solution design for high‑value GenAI use cases, and ensure that our data and AI platforms are secure, scalable, and aligned with business objectives. You will combine deep technical expertise with strong business acumen and change‑management skills to drive process efficiency, reduce cycle time, and lower cost across the product lifecycle.

About GDLS

General Dynamics Land Systems builds the combat vehicles and integrated technologies that give soldiers a decisive advantage. We design, engineer, and sustain advanced tracked and wheeled systems paired with modern electronic architecture, AI‑enabled capabilities, and autonomy‑ready technology.

From Abrams to LAV, Stryker to AJAX, robotic platforms to software solutions and beyond, our portfolio delivers proven performance and future‑ready modernization for customers around the world.

Join the people who design, build, and advance the systems that protect those who protect us. Our teams see beyond the horizon, solving problems before they become challenges.

Bring your talent. Bring your purpose.

Let’s shape the future of General Dynamics Land Systems together.

Key Responsibilities

Enterprise Data Fabric & Digital Thread Architecture

• Define and maintain the reference architecture for the GDLS enterprise data fabric, centered on Databricks and modern lakehouse capabilities (Delta Lake, streaming, advanced analytics).
• Architect an end‑to‑end digital thread that connects data and context from contracts and proposals through requirements, engineering, manufacturing, supply chain, and sustainment.
• Establish standards for data modeling, ingestion, transformation, and consumption (ETL/ELT, medallion architecture, reusable data products) to support analytics and GenAI use cases across the lifecycle.
• Ensure the data fabric supports traceability (e.g., contract → requirement → design → build → test → field performance) and enables closed‑loop feedback into engineering and operations.

Generative AI Strategy & Solution Design

• Partner with business, engineering, manufacturing, and supply chain leaders to identify, prioritize, and architect GenAI solutions that drive measurable process efficiency, cycle‑time reduction, and cost savings.
• Design and implement GenAI architectures leveraging LLMs, Databricks, vector databases, and retrieval‑augmented generation (RAG) to securely use enterprise data from the digital thread.
• Develop patterns for GenAI‑enabled use cases such as:
• Contract and requirements analysis, summarization, and impact assessment.
• Engineering knowledge retrieval and design decision support.
• Manufacturing work instruction generation and change impact analysis.
• Supply chain risk analysis, supplier insights, and exception handling.
• Sustainment and field support knowledge assistants using maintenance and telemetry data.
• Define integration patterns for embedding GenAI capabilities into existing PLM, ERP, MES, SCM, and sustainment tools via APIs and microservices.

Data Governance, Security & Responsible AI

• Collaborate with cybersecurity, legal, export control, and compliance teams to define and enforce data and AI governance, including access controls, data classification, and protection of sensitive and export‑controlled information.
• Implement guardrails for responsible AI use, including model input/output controls, content filtering, and monitoring for misuse or policy violations.
• Drive improvements in data quality, metadata management, lineage, and master data that directly support reliable AI and analytics outcomes across the digital thread.

Platform Ownership & Operational Excellence

• Provide architectural leadership for Databricks and related data/AI platforms, including environment design, workspace organization, and integration with enterprise systems (PLM, ERP, MES, SCM, CRM, sustainment systems).
• Define and implement monitoring and observability for data and AI workloads (performance, reliability, model accuracy, drift, usage, and business impact).
• Guide the selection and integration of complementary tools (e.g., orchestration, catalog, BI, MLOps) to create a cohesive, efficient data and AI ecosystem.

Transformation, Change Management & Adoption

• Translate complex data and AI concepts into clear, practical guidance for business stakeholders and technical teams, with a focus on digital thread enablement and process improvement.
• Develop and support adoption plans, including training, documentation, and best‑practice playbooks for data engineers, analysts, and application teams using Databricks and GenAI.
• Champion a data‑driven, AI‑enabled culture by demonstrating measurable value (cycle‑time reduction, touch‑time reduction, cost per transaction, quality improvements) and helping leaders understand where and how to apply GenAI responsibly.

Collaboration & Leadership

• Build strong, trusted relationships with IT, engineering, manufacturing, supply chain, sustainment, and functional leaders to ensure data and AI strategies are tightly aligned with business priorities and digital thread roadmaps.
• Influence architectural decisions across programs and projects, balancing innovation with risk management, security, and long‑term sustainability.
• Mentor and coach technical staff in modern data architecture, Databricks best practices, GenAI engineering, and responsible AI principles.

Required Education & Experience

• Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, or a related technical field.
• 10+ years of progressive experience in data architecture, solution architecture, or related roles, including:
• Significant experience designing and implementing enterprise data platforms (data lakes, lakehouses, or data warehouses) in complex environments.
• Hands‑on experience architecting and deploying AI/ML solutions, with at least 3+ years focused on Generative AI, LLMs, or advanced NLP solutions.
• Demonstrated expertise in:
• Databricks (or equivalent modern data platform), including Delta Lake, notebooks, jobs, clusters, and integration with upstream/downstream systems.
• Data modeling, ETL/ELT pipelines, and integration patterns across heterogeneous enterprise systems (e.g., PLM, ERP, MES, SCM, CRM, sustainment/field systems).
• Modern cloud or hybrid architectures (e.g., containerization, microservices, APIs) and their application to data and AI workloads.
• Strong understanding of information security, data privacy, and compliance considerations related to data and AI in regulated or defense‑industry environments.
• Proven ability to:
• Translate business problems and process pain points into technical architectures and roadmaps that deliver measurable efficiency and cost improvements.
• Lead cross‑functional technical initiatives from concept through implementation, including stakeholder alignment and change management.
• Use data and metrics to evaluate solution performance, quantify business impact (cycle time, cost, quality), and inform architectural decisions.
• Excellent verbal and written communication skills, with the ability to explain complex technical topics to non‑technical stakeholders and influence decisions at multiple levels.
• Ability to manage multiple priorities, operate effectively in a fast‑paced environment, and work with minimal direction while maintaining strong alignment with enterprise standards.

Preferred Qualifications

• Experience supporting engineering, manufacturing, supply chain, or defense/aerospace organizations, particularly in secure or classified environments.
• Prior experience leading enterprise data platform, data fabric, or digital thread initiatives, including multi‑domain data integration and self‑service analytics enablement.
• Hands‑on experience with:
• Large Language Models (LLMs), vector databases, RAG architectures, and prompt engineering.
• MLOps / AIOps practices, including CI/CD for models, model monitoring, and lifecycle management.
• Modern data platforms and tools (e.g., Databricks, Snowflake, Synapse, or equivalent) and common data engineering frameworks (e.g., Spark, Python, SQL).
• Familiarity with:
• DoD or defense‑industry cybersecurity and compliance frameworks.
• Model risk management, responsible AI frameworks, and AI ethics considerations.
• Advanced degree in Computer Science, Data Science, Engineering, or Business, and/or relevant certifications (e.g., Databricks, cloud architect, data engineering, AI/ML).
• Demonstrated experience building and socializing AI and data standards, reference architectures, and best practices across a large organization, with a focus on digital thread, process efficiency, and cost reduction.

What We Offer

• A Total Rewards package that is impactful and built for you.
• Healthcare including medical, dental, vision, HSA, and flexible spending accounts.
• Competitive base pay and incentive pay that rewards individual and team performance, along with comprehensive benefits.
• 401(k) with company match up to 6%.
• Educational assistance.
• 9/80 work schedule (This position’s standard work schedule is a 9/80. The 9/80 schedule allows employees who work a nine‑hour day Monday through Thursday to take every other Friday off.)
• Ongoing learning opportunities and a rewarding work environment.
• Modern office environment with an onsite cafeteria including a Starbucks Café, remodeled fitness center, and outdoor fitness track.