Top AI Engineer Jobs

Lead Mastercard’s enterprise AI security strategy as the highest-level technical authority. Design security architectures, frameworks, standards, and secure development practices for AI systems, models, agents, LLM applications, and supporting infrastructure. Direct vulnerability assessments, red-team exercises, penetration testing, threat research, compliance alignment, governance, and remediation initiatives. Advise senior leadership, influence enterprise security direction, develop strategic recommendations, mentor technical teams, and lead proof-of-concept and innovation programs focused on emerging AI threats and defenses.

Lead design, development, deployment, and maintenance of responsible AI/ML solutions (including LLMs, generative models, chatbots, and agent orchestration) for provider-facing products. Provide technical leadership, mentor engineers, align solutions with enterprise AI strategy and compliance, integrate AI into cloud architectures, and ensure production reliability and operational excellence.

Design, implement, and operate production-grade agentic AI and LLM-enabled services on AWS. Build retrieval-augmented systems, APIs, and integrations; optimize performance, scalability, cost, and observability; implement evaluation, testing, guardrails, and secure development practices while collaborating across product and engineering teams.

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.

Lead enterprise AI engagements from opportunity discovery through production deployment, adoption, measurement, and continuous improvement. Design scalable AI systems, evaluate models and agentic architectures, establish engineering and responsible AI standards, and create reusable frameworks. Partner with executives and business teams, translate ambiguous problems into measurable outcomes, influence technical direction, and coach senior engineers and emerging technical leaders.

Leads the discovery, design, prototyping, deployment, adoption, and continuous improvement of AI solutions across Expedia Group. Partners with business and executive leaders to translate ambiguous challenges into measurable outcomes, establishes scalable AI and software engineering standards, evaluates models and architectures, develops reusable frameworks, and coaches senior engineers and emerging technical leaders. Ensures solutions address reliability, security, privacy, cost, observability, evaluation, responsible AI, and sustained business value.

Designs and accelerates AI-enabled capabilities across ITSM products, initially Incident, Change, and Problem Management. Translates workflows into practical solution architectures, evaluates automation and AI patterns, prototypes capabilities, and guides implementation. Partners with product, engineering, architecture, platform, and governance teams to ensure scalable, supportable, risk-aligned solutions. Coaches teams on AI design tradeoffs, influences strategy, and establishes repeatable patterns and success measures across multiple ITSM offerings.

Designs and accelerates AI-enabled capabilities across ITSM offerings, initially Incident, Change, and Problem Management. Translates workflows into scalable solution designs, evaluates deterministic automation, predictive, generative, agentic AI, and API approaches, and conducts prototypes and experiments. Produces architecture and implementation guidance, coaches product and engineering teams, influences senior stakeholders, and ensures solutions meet enterprise standards for scalability, supportability, risk, and measurable business outcomes.

Designs, prototypes, evaluates, and guides deployment of AI-enabled capabilities across ITSM offerings, especially Incident, Change, and Problem Management. Partners with product, engineering, architecture, platform, and governance teams to select appropriate automation, analytics, machine learning, generative AI, agentic AI, or API-based patterns. Creates solution artifacts, coaches teams, influences strategy, establishes scalable design patterns, and defines success measures while managing operational and risk considerations.

Develop and integrate AI-enabled features (LLMs, agents, ML) into applications, write and test production-quality code, troubleshoot performance, and collaborate with product and engineering teams. Support monitoring, evaluation, and continuous improvement of AI capabilities; may work nights/weekends as needed.

Design, build, and maintain customer-facing digital commerce services (checkout, billing, subscriptions, catalog, cart, orders). Use AI-assisted workflows for coding, testing, review, and documentation. Develop scalable, low-latency APIs and services, improve observability, automation, and engineering practices, and partner with cross-functional teams to deliver reliable solutions.

Builds, scales, and maintains machine learning platforms supporting model training, deployment, monitoring, and lifecycle management. Develops secure platform services, APIs, automation, and CI/CD pipelines while integrating data engineering and feature management capabilities. Ensures reliability, scalability, and performance, produces architecture documentation, collaborates with data scientists and product teams, and promotes AI-assisted engineering practices and standardized validation across the team.

Build production AI-powered analytics and natural-language intelligence on governed telematics, aftersales, and energy data. Design multi-agent systems, retrieval/grounding/reasoning/memory patterns, and reusable AI services. Partner with data product teams to ensure trust, explainability, scalability, and performance of AI experiences for enterprise users.

Designs, develops, and troubleshoots secure, scalable ML software systems and services. Builds production code, architecture artifacts, APIs, and distributed microservices; analyzes data to improve applications and system architecture. Uses AI-assisted development tools responsibly, validates generated outputs, and guides peers on secure engineering practices. Requires Python and ML framework expertise, cloud-native technologies, public cloud experience, distributed systems knowledge, and Cassandra or equivalent NoSQL experience. Preferred experience includes GPU workloads, model serving, distributed inference, model compression, and edge deployment.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Design and implement multi-step agentic AI workflows (orchestration, retrieval, tool use, state management) using frameworks like LangGraph/LangChain. Build retrieval pipelines over proprietary CRE data, evaluate and monitor model quality with human-in-the-loop processes, integrate Anthropic models on AWS Bedrock, implement guardrails and observability, and rapidly prototype features with Product and Design to deliver trustworthy, source-linked AI capabilities that perform real business actions.

Leads the design, development, testing, troubleshooting, and delivery of scalable Java software systems for Identity and Access Management. Produces architecture artifacts, applies secure coding and non-functional requirements, mentors engineers, and drives Agile delivery. Leads adoption of enterprise-approved AI-assisted development tools, establishing standards for validation, security, and responsible use. Collaborates with stakeholders, evaluates system dependencies, and implements continuous improvements across software delivery and automation practices.

Build and productize an internal agentic AI platform for research and simulation. Responsibilities include developing and evaluating multi-agent workflows, designing prompts and tool calling, integrating structured and unstructured data, deploying AI workloads to cloud infrastructure, creating reusable software tools, and documenting evaluation criteria. The role emphasizes reliable, traceable LLM workflows, cloud services, security, governance, and production-grade AI platform engineering.

Develop and maintain enterprise data-intensive and AI-enabled applications supporting product development and quality teams. Build optimized SQL transformations, Databricks pipelines, Java/Python backend services, APIs, and cloud-native applications using Kubernetes and Docker. Partner with AI teams to productionize ML and LLM solutions, implement testing, CI/CD, observability, security, and reliability practices, troubleshoot production issues, shape architecture, and mentor engineers.

Build and ship driver-facing AI experiences for GM vehicles using Kotlin and Android Automotive. Own features end to end across conversational behavior, on-device fulfillment, vehicle integration, UI, connected services, validation, and iteration. Prototype and productionize intelligent capabilities, diagnose issues across application and cloud boundaries, establish engineering patterns, collaborate cross-functionally, mentor engineers, and influence the roadmap for in-vehicle AI products.

Implement and embed data and AI governance across Databricks and Snowflake platforms. Partner with delivery pods to automate quality gates, lineage, monitoring, and risk triage. Translate policy into technical controls, build observability for data drift and distribution, and optimize governance processes to accelerate compliant deployments.

Lead customer-facing design, deployment, and operation of agentic AI solutions on the ServiceNow platform. Analyze customer workflows, architect integrations and guardrails, deploy solutions to production, verify reliability and security, establish reusable technical standards, mentor engineers, advise executives, and feed field learnings back into product and engineering.

Design, build, and deploy production AI agents and orchestration systems using tools like LangChain and LlamaIndex. Apply prompting, RAG, fine-tuning, and model selection. Architect agent reasoning and workflows, implement monitoring and evaluation, and integrate AI systems with backend and frontend services for scalable, reliable, compliant production deployments.

Design and deploy agentic AI systems that automate multi-step financial workflows. Architect agents using frameworks like LangChain and LlamaIndex, apply prompting, RAG, and fine-tuning, optimize reasoning and tool use, build feedback-driven systems, and integrate AI reasoning securely into production backend and frontend services.