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VP – Distinguished Engineer, Generative AI Engineering
Slate Auto. Envision, design, and ship Slate’s GenAI platform .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in designing and deploying GenAI platforms, with a strong focus on production-scale systems, multi-agent architectures, and AI integration in manufacturing environments. Proven leadership in technical decision-making, team building, and establishing engineering standards.
Highest-signal resume keywords
15+ Years Engineering Experience5+ Years Shipping Production GenAI SystemsDeep Hands-On Experience with LLM APIsExperience with Agent Orchestration FrameworksProduction-Scale Open-Source Model Deployment
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
GenAI Platform DesignMulti-Agent System DeploymentPrompt EngineeringComputer Vision SystemsReal-Time Inference PipelinesData ArchitectureFine-Tuning Domain-Specific ModelsClosed-Loop Learning SystemsVector Database ExperienceSensor Fusion
Soft Skills
Technical LeadershipTeam BuildingCollaborationCommunication
Tools & Technologies
OpenAIAnthropicGeminiLangChainLlamaIndexAWSGCPAzureSnowflakeDatabricks
Certifications & Qualifications
BS RequiredMS or PhD Preferred
Industry Keywords
GenAIManufacturing AIRobotic Process ControlEvaluation FrameworksObservability PipelinesEdge Deployment ArchitecturesSecurity and Compliance ArchitecturePhysical Simulation EnvironmentsData StacksEnterprise-Scale AI Platforms
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformOpen Source
About the role
Key responsibilities & impact- Envision, design, and ship Slate’s GenAI platform
- Make long-term architectural decisions for GenAI platforms, agentic systems, robot-to-model feedback loops, and embedded AI
- Partner with Vehicle Engineering, Manufacturing, and Quality to deploy measurable AI solutions
- Design and own the end-to-end GenAI platform, including context, data, model serving, agent frameworks, and evaluation pipelines
- Design AI systems for physical manufacturing, including agentic coordination and model personalization
- Embed AI into manufacturing through robotic process control, computer vision, predictive maintenance, and closed-loop feedback
- Build Slate’s proprietary data and context layer and unified data layer
- Deploy production-grade AI agents across vehicle engineering, manufacturing, supply chain, software development, and GTM
- Establish evaluation frameworks, guardrails, and observability practices
- Recruit and grow GenAI engineers, MLOps engineers, and applied scientists
- Review pull requests, make architecture decisions, and ship code alongside the team
- Establish technical standards, engineering practices, and hiring standards
- Contribute externally through open source, publications, or conference talks
- Report directly to the Chief Digital and Operations Officer and participate on the senior technology leadership team
Requirements
What you’ll need- 15+ years of engineering experience
- 5+ years shipping production GenAI systems
- 7+ years in a senior technical leadership role
- Work recognized outside the candidate’s organization
- BS required
- Deep hands-on experience with LLM APIs including OpenAI, Anthropic, and Gemini
- Production-scale open-source model deployment
- Experience with vector databases
- Experience with agent orchestration frameworks such as LangChain or LlamaIndex
- Prompt engineering for reliability
- Fine-tuning and training purpose-built domain-specific models
- Experience with GenAI evaluation frameworks, guardrails, and observability pipelines
- Experience designing and deploying production multi-agent systems
- Experience deploying AI systems on physical hardware or in industrial environments
- Computer vision systems for manufacturing quality assurance
- Closed-loop learning systems with physical-world outcomes
- Robot-to-model feedback architectures
- Familiarity with physical simulation environments such as Isaac Sim or MuJoCo
- Sensor fusion and multimodal data handling
- Real-time inference pipelines with hard latency constraints
- Edge deployment architectures under intermittent connectivity
- Data architecture using modern data stacks such as Snowflake, Databricks, and dbt
- Vector stores and streaming data pipelines
- Architecture-level cloud infrastructure experience with AWS, GCP, or Azure
- Enterprise-scale AI platforms serving thousands of concurrent users
- Multi-tenant model serving architectures
- Security and compliance architecture for proprietary manufacturing IP
- Platform engineering practices including SDKs, abstraction layers, and self-service tooling
- MS or PhD in a relevant field preferred, not required
Benefits
Comp & perks- Medical insurance
- Dental insurance
- Vision insurance
- Life insurance
- Disability insurance
- Vacation
- 401k
- Equity program eligibility
- Discretionary annual incentive program eligibility
- Reasonable accommodation for qualified individuals with disabilities