Staff Cloud and AI Solutions Architect

We need to output HTML fragment with sections: h3 headings for sections present: About the role? The posting includes "Production Mapping is looking for..." which is summary. Then "The role" section. Then "What you’ll do". Then "Required Qualifications". Then "Preferred Qualifications". Then "What will make you successful". Then "Compensation:" includes salary range. Then "Benefits:" includes description. Also "About GM", "Why Join Us", "Benefits Overview", "Non-Discrimination and Equal Employment Opportunities (U.S.)", "Applicants in the recruitment process may be required...", "Accommodations". According to rules, we must remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. So we should drop sections like Non-Discrimination, Accommodations, About GM maybe? The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." So we should remove EEO, Non-Discrimination, Accommodations, maybe "About GM" and "Why Join Us" and "Benefits Overview" might be considered boilerplate? They are not strictly legal but could be considered company overview. The instruction says preserve ALL substantive information; do NOT invent or summarize away detail. However, "About GM" and "Why Join Us" might be considered fluff but still substantive? The instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate. "About GM" is not legal but could be considered boilerplate. However, they said "Prefer a few substantive sections over many thin ones." Might keep only core job description sections: About the role (maybe the initial summary), Responsibilities (What you’ll do), Requirements (Required Qualifications), Preferred Qualifications, Benefits, Pay (Compensation). Also maybe "What will make you successful" could be considered part of Requirements or separate? Could be a section like "Success Factors". But we need to decide. We must not repeat the job title as a heading. The page already shows it, so we don't add a heading for title. We must not put Job ID/requisition numbers as headings. We need to use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can use those exact headings if content matches. We have content for: - About the role: The initial summary paragraph: "Production Mapping is looking for a Staff Cloud and AI Solutions Engineer to build and scale future-ready mapping foundations..." That's about the role. - Responsibilities: "What you’ll do" list. - Requirements: "Required Qualifications". - Qualifications: Could be "Preferred Qualifications" maybe as "Preferred Qualifications" heading? The list includes "Preferred Qualifications". Could treat as "Qualifications" but we already have Required Qualifications. Could have separate "Preferred Qualifications" heading? The instruction list includes Qualifications as a possible heading. Could use "Qualifications" for required, and maybe "Preferred Qualifications" as a subsection? But we need to follow instruction: Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should map content to those headings. We have Required Qualifications -> Requirements. Preferred Qualifications could go under Qualifications? Or we could have a separate "Preferred Qualifications" but not in list; maybe we can still use "Qualifications" heading and include both required and preferred? But that might mix. Better to have "Requirements" for required, and "Preferred Qualifications" as a separate heading? The instruction says "e.g." not exhaustive; we can use other headings if needed? It says "Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." It implies those are examples; we can use other headings if appropriate, but better to stick to those. However, we have "What will make you successful" which could be under "Skills" maybe? Or we could create a "Skills" section. But we have no explicit skills list; we could put that under Skills. Benefits: we have benefits description. Pay: we have compensation salary range. Schedule: not present, so we should not include Schedule section. We must not include sections with filler like "commensurate with experience". We have actual salary range. We need to preserve all substantive information; we must not drop details like bullet points. We need to format lists using

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  . We need to remove boilerplate: EEO, Non-Discrimination, Accommodations, About GM, Why Join Us, Benefits Overview, etc. Also remove "Applicants in the recruitment process may be required..." (application instructions). Also remove "To learn more, visit How we Hire." etc. Thus final HTML will have sections:

  About the role

  Production Mapping is looking for a Staff Cloud and AI Solutions Engineer to build and scale future-ready mapping foundations that support the expansion of our mapping databases and the next generation of software-defined vehicle experiences. This role will combine deep software and cloud engineering with data-platform architecture and practical AI enablement. You will design scalable foundations for mapping data, create reusable services and workflows, and help transform the team’s existing knowledge, tools, and engineering practices into secure, production-ready Agentic AI solutions. Your work will help mapping teams move faster, improve data quality and traceability, and use cloud-based intelligence in their day-to-day development and operations. The ideal candidate is a hands-on technical leader who can work across data engineering, backend services, cloud infrastructure, distributed systems, developer tooling, and AI-enabled workflows. Experience in automotive, mapping, ADAS, SDV, robotics, or other data-intensive domains is highly desirable.

  Responsibilities

  • Define and evolve the target architecture for scalable mapping data foundations, including data models, storage patterns, ingestion and transformation pipelines, APIs, data access, metadata, lineage, quality controls, and governance.
  • Build, productionize, and scale reusable cloud-native solutions and services that support the growth, availability, performance, security, and cost efficiency of mapping databases.
  • Establish data contracts, validation frameworks, observability, and operational standards that make mapping data trustworthy and easier to consume across engineering teams.
  • Design and implement cloud-first solutions using infrastructure as code, automated deployment, containerized services, CI/CD, monitoring, and production-readiness practices.
  • Partner with map creation, map delivery, validation, simulation, embedded software, data science, and platform teams to understand their data needs and deliver integrated solutions.
  • Identify high-value opportunities to apply AI to everyday engineering workflows, including data discovery, technical search, map-data analysis, validation support, diagnostics, release readiness, incident triage, and engineering productivity.
  • Design and productionize knowledge-grounded Agentic AI solutions that can use approved documentation, code, metadata, telemetry, and operational knowledge to support multi-step engineering tasks.
  • Help define the architecture and operating model for smart agents, including retrieval, tool use, orchestration, access controls, evaluation, observability, human oversight, and safe deployment.
  • Create patterns that allow AI agents and data services to scale reliably in the cloud across environments and teams.
  • Build reference implementations and reusable frameworks so teams can adopt cloud, data, and AI solutions without repeatedly solving the same foundational problems or creating unnecessary central dependencies.
  • Lead architecture discussions, design reviews, and focused technical workshops across teams; clarify ownership boundaries and resolve cross-team technical seams.
  • Mentor engineers through technical guidance, design feedback, code reviews, and examples of strong engineering practices.
  • Balance near-term delivery with long-term maintainability, solution simplification, security, reliability, and responsible use of AI.

  Requirements

  • Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Electrical Engineering, Data Engineering, Artificial Intelligence, or a related technical field; equivalent practical experience may be considered.
  • 10+ years of professional software engineering experience building and operating production systems.
  • Demonstrated experience providing senior technical leadership across architecture, design, implementation, and production operations.
  • Strong experience with cloud-native and distributed systems, including scalable services, asynchronous or event-driven workflows, data-intensive applications, and reliability engineering.
  • Hands-on experience with at least one major cloud platform, such as Azure, AWS, or Google Cloud Platform.
  • Experience with infrastructure as code, containers or Kubernetes, CI/CD, automated testing, observability, cloud security, and production operations.
  • Strong programming experience in one or more languages such as Python, Go, Java, C++, or a comparable production language.
  • Experience designing data platforms, data services, or large-scale data pipelines, including data modeling, storage, transformation, APIs, data quality, and governance.
  • Experience applying AI, machine learning, generative AI, retrieval-augmented generation, or workflow automation to practical software engineering or business problems.
  • Understanding of Agentic AI or multi-step workflow patterns, including tool integration, retrieval, orchestration, evaluation, monitoring, and access control.
  • Demonstrated ability to influence technical direction across teams without relying on formal organizational authority.
  • Strong written and verbal communication skills, with the ability to explain complex technical decisions to both technical and non-technical stakeholders.

  Qualifications

  • Experience in automotive, software-defined vehicles, ADAS, autonomous driving, mapping, geospatial systems, robotics, simulation, or another safety- and scale-sensitive domain.
  • Experience building or expanding mapping databases, geospatial data platforms, map-production pipelines, map validation systems, or data-delivery services.
  • Experience with Azure, Databricks, Terraform, Kubernetes, Spark or PySpark, Kafka or other event-streaming technologies, and modern data-lake or lakehouse architectures.
  • Experience with vector databases, semantic search, knowledge graphs, metadata platforms, document intelligence, or knowledge-grounded AI applications.
  • Experience designing and operating internal developer platforms, engineering productivity tools, or self-service cloud capabilities.
  • Experience with model-training, simulation, offline analytics, digital-twin, or other high-volume data consumers.
  • Experience defining AI quality, security, privacy, governance, and responsible-use practices for internal engineering tools.
  • Experience scaling reusable engineering capabilities across multiple teams and managing tradeoffs among delivery speed, performance, reliability, and cost.

  Skills

  What will make you successful: You are a builder who can move between architecture, code, infrastructure, data, and operational outcomes. You can distinguish a reusable engineering problem from a one-off team problem and focus effort where it creates the most leverage. You are comfortable working with ambiguity and turning emerging AI capabilities into reliable products with measurable adoption. You bring strong systems thinking: data quality, security, observability, reliability, cost, and user experience are considered together. You communicate clearly, create alignment, and use influence rather than authority to drive adoption. You value practical delivery and can simplify complex technical choices without losing the long-term architectural direction.

  Pay

  The salary range for this role: is $189,300 to $290,700. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.

  Benefits

  GM offers a variety of health and