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About This Role
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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At General Dynamics Land Systems, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Dynamics Land Systems AI Hiring
General Dynamics Land Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sterling Heights, MI, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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