Top AI & Machine Learning Jobs

Leads a Gen AI engineering team developing intelligent agents, digital assistants, and AI-driven customer experiences. Provides technical direction, manages resources, oversees team performance and development, establishes engineering best practices, and owns product quality. Requires strong software engineering, integration, testing, organizational leadership, program management, and cross-functional project delivery experience in agile, matrixed environments.

Leads development and deployment of generative AI solutions, including assistants, AI processing pipelines, tools, and scalable software services. Builds Python and Go applications using FastAPI and Gen AI frameworks, deploys AWS infrastructure with CloudFormation, and manages GitHub Actions CI/CD pipelines. Develops edge-device services, supports machine learning pipelines with Apache Airflow, and collaborates with data scientists to productionize analytics solutions. The role also requires software architecture, testing, technical writing, and coordination of complex cross-functional projects.

Develop and deploy generative AI assistants, processing pipelines, tooling, and software using Python or Go. Build FastAPI services, edge-device components, CI/CD pipelines, and AWS infrastructure. Apply LLMs, prompt engineering, and modern agent architectures using LangChain and LangGraph. Collaborate with data scientists to productionize scalable analytics and AI solutions, and assist with Airflow-based machine learning pipelines. The role also requires software architecture, testing, technical writing, and cross-functional collaboration.

Leads Rental Insights and Analytics by developing BI and analytics roadmaps, analyzing complex business data, defining KPIs, gathering requirements, and delivering actionable insights. The role applies Python, SQL, statistical methods, machine learning, cloud data platforms, and visualization tools to optimize rental revenue, dealer operations, product utilization, and customer growth. It also requires collaboration with senior leadership and product teams, data storytelling, and strong technical problem-solving.

Leads advanced data science initiatives for Caterpillar’s Service Events domain. Develops and productionizes machine learning and statistical models using large-scale structured and unstructured data, including NLP and predictive analytics. Directs data acquisition, transformation, modeling, and optimization efforts; defines analytical problems and success criteria with stakeholders; communicates findings through visualization and storytelling; and provides technical leadership, coaching, and mentoring across cross-functional teams.

Leads Caterpillar’s Advanced Technology Pod within AI R&D, setting the applied AI vision and shaping high-impact initiatives. Oversees production-intent prototypes, reference architectures, evaluation standards, and responsible AI practices. Manages senior data scientists and AI engineers while partnering with product, engineering, business, research, customers, dealers, and technology partners. Translates emerging AI research into scalable, production-ready concepts and communicates technical strategy and business outcomes to executives.

Develop and optimize generative AI and large language model solutions, including fine-tuning, agentic workflows, retrieval-augmented generation, and experimentation. Research emerging algorithms, evaluate model performance using automated and human-in-the-loop methods, implement safety guardrails, and integrate AI services with cloud platforms. Mentor junior data scientists and guide technical approaches for AI-driven products.

Support Aftermarket digital products by gathering requirements, meeting customers, delivering training, developing data code, ensuring data quality/governance, testing, and analyzing large datasets to provide business insights and resolve technical product issues.

Lead and define end-to-end perception architecture for autonomous machine systems. Own sensor integration, sensor fusion, perception ML, scene understanding, data flow, validation, and safety. Guide a Perception Architecture Team, create roadmaps, drive architecture tradeoffs for edge deployment, and partner cross-functionally to ensure perception is deployable, testable, and safe in real-world jobsites.

Provide technical leadership for architecture and integration of Physical AI services, digital twin and simulation data exchange, and cloud-native backend platforms. Design and build scalable microservices and APIs (Java, Spring Boot, Python), enforce API standards, drive engineering excellence (CI/CD, observability), mentor engineers, and guide integration, reliability, and platform simplification across teams.