Top AI & Machine Learning Jobs

Provides global technical leadership for Physical AI and cyber-physical product assurance. Develops standards, certification programs, assessment methodologies, testing criteria, and customer-facing services for robotics, autonomy, drones, smart mobility, and connected intelligent products. Evaluates real-world system behavior, hazards, failure modes, safety controls, human interaction, robustness, and lifecycle changes. Advises customers, regulators, and standards bodies while building global evaluator training, delivery guidance, and scalable assurance capabilities.

Provides global technical leadership for digital AI standards, certification, assurance, training, advisory, and assessment services. Develops AI governance and lifecycle assurance methodologies covering predictive and generative AI, LLMs, agents, cloud AI, model risk, testing, transparency, fairness, privacy, security, robustness, and operational resilience. Translates regulations, standards, emerging technologies, and customer needs into scalable offerings while engaging customers, regulators, standards bodies, and internal stakeholders.

Supports data collection, cleaning, validation, analysis, visualization, reporting, and basic predictive modeling. The role collaborates with business, data engineering, and technical teams to translate requirements into analytical solutions, communicate insights, improve data quality, and document processes. It uses SQL, Python, Power BI, Excel, and Agile tools while developing experience with statistical methods, cloud data platforms, governance, and emerging AI technologies.

Design and refine prompts, experiment with Microsoft AI Foundry, analyze workflows to find automation opportunities, assess data quality and governance, propose AI-driven features, connect models to applications, document experiments, and collaborate with cross-functional teams.

Lead and develop globally distributed Data and AI teams to deliver production AI, analytics, and automation solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to translate strategy into measurable delivery outcomes.

Lead and develop globally distributed Data and AI teams to deliver production Data/AI solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to set priorities, roadmaps, and delivery metrics.

The Data Scientist will analyze complex datasets, build predictive models, collaborate with stakeholders, and present insights for business decisions.

Lead the design, development, deployment, and continuous improvement of scalable production AI solutions. Build LLM, RAG, agentic AI, machine learning, and document intelligence systems using Python, Azure, DevOps, IaC, Docker, Kubernetes, and MLOps. Own architecture, testing, evaluation, monitoring, security, governance, and operational support while translating business needs into measurable outcomes. Provide technical leadership, mentorship, stakeholder collaboration, and influence enterprise AI engineering standards and platforms.

Design, deliver, and continuously improve scalable production AI solutions using LLMs, RAG, agentic AI, machine learning, and document intelligence. Build cloud-native systems on Azure using software engineering, architecture, DevOps, and MLOps practices. Own AI platforms and models across deployment, monitoring, security, governance, and optimization. Lead technical initiatives, collaborate with product and business teams, mentor engineers, and translate business needs into measurable outcomes.