Job Summary: 
Join the AI Engineering team of Cat Digital and take charge of leading and coordinating a team dedicated to building advanced generative AI solutions—including intelligent agents, digital assistants, and other innovative capabilities. You will shape the development of cutting-edge products that redefine how customers and dealers interact with technology through AI-driven experiences. 

 
 What You Will Do: 

• Providing strong technical support and clear direction to ensure the team is aligned with company goals and capable of delivering advanced AI projects. 

• Managing resources efficiently to ensure projects are completed on schedule and to a high standard. 

• Overseeing the performance of both individual team members and the team as a whole, fostering a culture of learning by identifying and addressing training and development needs. 

• Establishing and supervising the implementation of engineering best practices to maintain consistency and excellence in development processes. 

• Taking ownership of the quality of engineering products, making sure all solutions are robust, reliable, and meet high standards. 

 
 What You Will Have: 

• Software Development: Expert knowledge of software development tools and activities; ability to produce software products or systems in line with product requirements. 

• Software Integration Engineering: Extensive knowledge of software integration processes and functions; ability to design, develop and maintain interfaces and linkage to alternative platforms and software packages. 

• Software Quality Assurance and Testing: Extensive knowledge of software quality assurance and testing; ability to apply appropriate processes, tools, and techniques for assuring a high level of quality in computer software products and systems. 

• Organizational Leadership: Extensive knowledge of leadership concepts and ability to use strategies and skills to enlist others in setting, embracing and achieving objectives. 

Considerations For Top Candidates: 

• Extensive software engineering leadership experience with leading AI and software teams in agile, matrixed organizations 

• Demonstrated track record of delivering GenAI solutions and enterprise-scale products across hybrid cloud and embedded/edge environments. 

• Experience delivering complex cross-functional digital projects in matrixed organizations. 

• Proven experience with project management concepts including project charters, scheduling and planning projects and successful completion.  

• Excellent interpersonal skills are required to deal with sensitive issues, develop others, or influence others inside and outside the department to take specific actions. 

• Should have strong project management skills, team leadership skills, excellent communication skills, strong analytical and organizational skills. 

• Extensive experience with delivering software solutions at scale and complete grasp of fundamental concepts such as CI/CD, unit testing, integration testing, feature flags, blue/green deployments, canary deployments; experience with incident management 

• Solid software engineering and distributed systems knowledge; understanding of scalable system design, APIs, microservices, and cloud/edge architectures, enabling effective integration of AI components into production-grade applications. 

• Expertise in enterprise platforms and data ecosystems, hands-on experience with delivering data pipelines and data objects 

• Working knowledge of major cloud platforms such as Azure, AWS, or GCP, including designing and managing scalable, secure AI workloads using services such as Azure Foundry, SageMaker, Bedrock, EC2, and S3 

• Experience with Snowflake, including AI compute capabilities such as Cortex. 

• Deep knowledge of GenAI system design, including prompt engineering patterns such as system prompts, few-shot, chain-of-thought, and structured output; agentic system concepts including tool use, planning, multi-agent orchestration, memory, guardrails; vector databases concepts such as chunking, embeddings; patterns such as RAG (sparse, dense, hybrid); fine-tuning approaches such as LoRA. 

• Deep knowledge of GenAI model types, including LLMs, SLMs, speech, multimodal, and real-time models 

• Experience with agentic orchestration frameworks such as LangChain, LangGraph 

• Familiarity with MCP and A2A protocols 

• Strong expertise in AI evaluation, model selection, benchmarking, and optimization across open-source and commercial model ecosystems. 

• Strong foundationin AI productsevaluation and performance measurement, with deep familiarity with evaluation methodologies, including GenAI-specific metrics (e.g., RAG quality, output reliability), A/B testing, and human-in-the-loop validation to ensure robustness and consistency in production systems.