Staff Cloud and AI Solutions Architect

Job Description

Posting summary

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.

As a Staff Cloud and AI Solutions Engineer, you will provide technical leadership for the solutions, services, and engineering patterns that make Production Mapping data more scalable, trusted, discoverable, and useful.

You will help define the architecture for a future-ready mapping database ecosystem, including data ingestion, transformation, storage, access, quality, lineage, governance, and delivery to downstream consumers. You will also identify practical opportunities to apply AI and agentic workflows to engineering work, using the knowledge base that already exists across documentation, code, metadata, operational data, and team practices.

This is a senior individual-contributor role with broad influence and hands-on delivery responsibility. You will set technical direction, build reference implementations, establish reusable standards, and partner with multiple teams to move solutions from concept to production. Success will come from creating durable capabilities that teams adopt—not from becoming the owner of every mapping system or every AI initiative.

• 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.

• 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.

• 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.

• 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.

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.

• 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 wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more





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