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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Generative AI / Enterprise Data Senior Architect - **Company:** General Dynamics Land Systems - **Location:** Sterling Heights, MI, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Engineering, Encodings, Cyber Security, Information Systems, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Warehousing, Decision Support Systems, Monitoring of Systems, Python (Programming Language), Machine Learning, Meta-Data Management, SQL Databases, Data Streaming, Enterprise Data Management, Data Classification, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Generative AI, Web Filtering, Microsoft Fabric, Containerization, Data Lakes, AI Platforms, Information Technology, Data Management, Machine Learning Operations, Azure Synapse Analytics, Data Pipelines, Databricks, Microservices - **Published:** August 4, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/17809878?backUrl=%2Fcareer%2F17809878%2FGenerative-Ai-Enterprise-Data-Senior-Architect-Michigan-Sterling-Heights ## About the Role * 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. * Handson 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 defenseindustry environments. Proven ability to: * Translate business problems and process pain points into technical architectures and roadmaps that deliver measurable efficiency and cost improvements. * Lead crossfunctional 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 nontechnical stakeholders and influence decisions at multiple levels. Ability to manage multiple priorities, operate effectively in a fastpaced environment, and work with minimal direction while maintaining strong alignment with enterprise standards., * 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 multidomain data integration and selfservice analytics enablement. * Handson 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 defenseindustry 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. ## Description 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 endtoend 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 closedloop 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, cycletime reduction, and cost savings. * Design and implement GenAI architectures leveraging LLMs, Databricks, vector databases, and retrievalaugmented generation (RAG) to securely use enterprise data from the digital thread. * Develop patterns for GenAIenabled 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 exportcontrolled 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 bestpractice playbooks for data engineers, analysts, and application teams using Databricks and GenAI. * Champion a datadriven, AIenabled culture by demonstrating measurable value (cycletime reduction, touchtime 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 longterm sustainability. * Mentor and coach technical staff in modern data architecture, Databricks best practices, GenAI engineering, and responsible AI principles. ## Related Videos - [A Brief History of Data Storage](https://www.wearedevelopers.com/videos/974-a-brief-history-of-data-storage) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [JSON and Beyond](https://www.wearedevelopers.com/videos/968-json-and-beyond) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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