AI Engineer

• Build and nurture relationships with AI developers, data scientists, and technical communities worldwide
• Present at conferences, meetups, and industry events to showcase Intel's AI technologies and developer tools
• Develop compelling technical content including blog posts, tutorials, videos, and documentation
• Act as the voice of developers within Intel, influencing product roadmaps and developer experience improvements
• Drive adoption of Intel AI platforms by creating resources, samples, and best practices that accelerate developer success
• Engage with the developer community to position Intel as the platform of choice for AI development

AI Developer Evangelist

• Design, build, and deploy AI-powered tools and automations for HR and Talent use cases, including intelligent workflow automations, employee-facing chatbots, document processing solutions, and decision-support systems
• Identify opportunities to improve manual or repetitive HR workflows through scalable AI/ML-driven automation solutions
• Develop integrations between AI solutions and enterprise systems including Workday, ATS platforms, ServiceNow Ticketing, and other internal tools ensuring seamless data flow and user experience
• Partner directly with HR Operations and cross-functional stakeholders to gather requirements, prototype solutions, run pilots, and drive successful adoption
• Continuously monitor and improve deployed tools based on user feedback, operational metrics, and evolving business needs
• Create technical documentation, implementation guides, and operational handoff materials to support long-term maintainability and scalability.

Principal AI Engineer, HR Automation

• Own the end-to-end administration of the Association’s primary AI platform and Microsoft Copilot
• Manage user provisioning and deprovisioning, SSO configuration, permission tiers, and seat allocation across standard and Pro/Max license tiers
• Ensure platform configurations meet enterprise security standards and Responsible AI guidelines
• Design, build, and maintain integrations between AI platforms and core enterprise systems
• Partner with Enterprise Risk to ensure all AI platform configurations meet data security policies
• Track platform consumption patterns and generate usage analytics
• Serve as the primary technical point of contact with AI platform vendors
• Lead technical due diligence when new AI tools are proposed
• Own the build and maintenance of internal CoE tooling
• Forecast platform capacity needs as adoption grows

Director of AI Platform Operations

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization.
• You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.
• This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning.
• Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.
• You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Senior AI Engineer, Agentic Evaluation – V&V

• As a technical lead, you will own the architecture and technical roadmap for object perception systems used by the robot in production.
• Design, develop, and deploy machine learning algorithms for multi-object detection, scene understanding, and 6-DoF object pose estimation.
• Evaluate and drive adoption of state-of-the-art perception models.
• Promote best practices in architecture, design, and testing to deliver high-quality, scalable software.
• Optimize deep neural networks and associated data processing to run efficiently on embedded systems.
• Collaborate with navigation, manipulation and hardware teams to align perception capabilities with product requirements.

Staff AI Engineer, Perception

• Play a key role in building the internal platform which supports training and deploying AI models across the entire organization.
• Take ownership of defining the platform to enable AI model fine-tuning and batch inference by building the SDKs and supporting the infra.
• Manage cross-cutting stakeholder relationships, prioritizing customer needs first.
• Navigate ambiguous, hairy, and technical problem spaces.
• Jump into domains, languages, and problem areas that might be new and unfamiliar.

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