Executes and improves the tooling, processes, and AI initiatives that govern how Zebra’s support knowledge performs across self-service and AI-driven support experiences, focusing on knowledge gap execution, AI performance monitoring, and prompt refinement.
Responsibilities
Implement, configure, and optimize knowledge and AI content tooling.
Independently monitor AI content performance signals—including retrieval gaps and answer quality.
Assess AI-generated responses for accuracy, grounding, and consistency.
Write, test, and refine prompts to improve how knowledge is surfaced.
Drive the knowledge gap strategy by conducting regular audits to identify outdated or underperforming content.
Requirements
Bachelor’s degree in Information Science, Information Management, Computer Science (AI/Data Science track), or a related field
Join an international, cross-functional team to build AI-driven solutions for background screening, splitting time between delivering new products and supporting production ML workloads.
Responsibilities
Work within a Scrum team on new products and initiatives, from design through delivery.
Provide dedicated support for existing production AI/ML workloads, ensuring reliability, performance, and continuity.
Build secure, well-optimized solutions using modern AI tooling.
Develop and productionize ML models, and design end-to-end testing strategies to hold delivered solutions to a high quality bar.
Deliver API integrations and data pipelines, optimizing for latency, throughput, and cost.
Leading the design and delivery of next-generation scalable services, driving transition into AI-native engineering using Python, React, and GCP.
Compensation
The starting base salary (for non-sales roles) or base salary + commission target (for sales/com-missioned roles) is expected to be the annual range listed below $145,000.00 - $235,500.00/yr
Responsibilities
Drive architectural vision and deployment of high-quality, scalable software assets using Python and React
Architect and govern advanced agentic systems using Vertex AI Agent SDK, LangChain, or LangGraph
Establish enterprise-wide AI Evaluation (Evals) frameworks and automated benchmarking pipelines
Define organizational strategy and architecture for data retrieval pipelines (vector embeddings, semantic search)
Establish enterprise-wide prompt engineering standards and LLM security frameworks
Requirements
Bachelor's degree in Computer Science or a related field
Lead AI Engineer responsible for setting vision, building team, and delivering AI solutions at scale in semiconductor manufacturing.
Responsibilities
Define AI strategy and architecture for integrating machine learning into core engineering and manufacturing processes.
Partner with tool, process, and applications engineers to map as-is processes and define a to-be AI/automation architecture and deliver measurable outcomes.
Ship agentic assistants for use-cases. Stand up LLM + RAG + tool integrations that help engineers accelerate tool operation/setup/maintenance and explain trade-offs.
Lead projects across predictive maintenance, computer vision, LLM-based engineering assistants, process optimization, simulation and digital twins.
Build retrieval-augmented AI assistants to query internal knowledge bases, tools, and logs.
Develop transformative AI/ML solutions to address clients' business requirements and challenges, bridging research and production to create ground-breaking products and services.
Responsibilities
Develop transformative AI/ML solutions to address clients' business requirements and challenges
Close the gap between AI research and production to create ground-breaking new products, features and solve problems for our customers with GenAI
Build and implement machine learning models and prototype solutions for proof-of-concept
Scale existing AI models into production on a variety of cloud platforms with GenAI
Analyze and resolve architectural problems, working closely with engineering, data science and operations teams
Requirements
Bachelor's degree in computer science engineering, information technology or BSc in Computer Science, Mathematics or similar field
Master’s degree is a plus
Nice to have: Certifications in AI/ML or GenAI (optional)
Nice to have: Familiarity with generative models, prompt engineering, and fine-tuning techniques
Hands‑on AI Engineer responsible for designing, developing, and deploying AI agents and intelligent automation solutions across the full development lifecycle, integrating with Microsoft Azure and Microsoft 365 services.
Responsibilities
Design, develop, and deploy AI agents capable of performing multi‑step tasks and automating business workflows
Build agents using Microsoft Foundry, Azure AI Agent Service, LangGraph, Semantic Kernel, and similar frameworks
Develop agent capabilities including tool calling, function execution, retrieval‑augmented generation, memory management, and workflow orchestration
Integrate agents with internal applications, external APIs, databases, and enterprise systems
Test, evaluate, and optimize agent accuracy, reliability, and performance
Lead the design, development, and deployment of GenAI solutions and architect cloud-native applications for Visa’s Legal Technology organization.
Responsibilities
Lead the design, development, and deployment of GenAI solutions, including LLM integration, prompt engineering, RAG systems, and domain‑specific model customization.
Evaluate emerging AI capabilities and prototype innovative solutions to accelerate business transformation.
Drive experimentation frameworks that validate model performance, user experience impact, and production feasibility.
Architect cloud‑native applications capable of handling large‑scale, high‑availability workloads.
Oversee performance optimization, distributed processing, caching strategies, and operational monitoring.
