Top Artificial Intelligence Jobs

Design, develop, and deploy production AI/ML solutions for underwriting and healthcare benefits modernization. Build RAG pipelines, integrate LLMs, support agentic AI using MCP and agent-to-agent protocols, and create secure, high-performance APIs for machine learning capabilities. Collaborate with software, data engineering, and underwriting teams to deliver scalable microservices and pipelines while ensuring model availability, security, monitoring, testing, and performance.

Design, build, and scale a multi-tenant AI Ops platform: develop high-throughput microservices, GraphQL/REST APIs, backend infrastructure, data systems, telemetry, and CI/CD deployments for operationalized ML/AI services.

Lead identification, development, piloting, and scaling of AI use cases for channel strategy and optimization. Partner with analytics, product, and controls teams to integrate AI, establish ROI metrics, build prompt libraries, and drive adoption across marketing channels while documenting best practices.

Leads FreeWheel’s Built-In AI Platform and Data Experiences portfolio for premium video advertising. Owns product strategy, roadmap, delivery, business performance, and customer outcomes across interconnected product suites. Builds and manages a product management organization, partners with engineering and data science on AI capabilities, and develops reporting, dashboards, visualizations, alerts, and responsible AI practices. Represents the portfolio with executives, customers, and partners while prioritizing investments, defining KPIs, and guiding products from inception through market deployment.

Build and enhance AI/ML platform capabilities spanning model registries, feature registries, governance, data preparation, annotation, lineage, and auditability. Develop cloud-native microservices, APIs, and large language model-powered workflows using retrieval and agent patterns. Improve platform reliability, security, observability, performance, and developer experience while collaborating with engineers, architects, product managers, data scientists, and research partners.

Leads product strategy and a product management team for enterprise AI/ML and machine learning platforms. Defines Generative AI and ML platform vision, evolves capabilities for data scientists and software developers, drives platform adoption, and partners with adjacent platform leaders on an integrated ecosystem. The role requires deep technical expertise in AI/ML infrastructure, Generative and Agentic AI, cloud and open-source technologies, people leadership, and AI risk management.

Develop and deploy machine learning models for credit decisions and fraud across the customer lifecycle. Apply deep learning, transformer models, and LLMs on large-scale data platforms. Build explainability and fairness frameworks, collaborate with risk, governance, technology, research, and senior leadership, and support production deployment, monitoring, drift detection, and auditability.

Lead the Scheduling product roadmap, shipping mobile calendar and AI-native scheduling features. Balance platform reliability and new capabilities, partner with Engineering/Design/GTM, and drive metrics across booking volume, calendar utilization, and AI engagement to increase GMV and provider retention.

Design, develop, and deploy production AI and machine learning solutions for underwriting and healthcare benefits modernization. Build RAG pipelines, integrate LLMs and agentic AI capabilities, create secure high-performance APIs, and collaborate with software, data engineering, and underwriting teams. Optimize model performance, availability, security, monitoring, and testing while evaluating emerging AI technologies and supporting scalable ML platforms.

Own CoreWeave’s AI training infrastructure narrative, positioning, competitive messaging, customer evidence strategy, go-to-market approach, and sales enablement. Translate Kubernetes, scheduling, bare-metal performance, and distributed training capabilities into compelling messaging for AI research leaders, platform engineers, developers, and technical architects. Partner with technical teams on benchmarking, collaborate with Sales, Solutions Architects, and DevRel, and measure impact through deal velocity, contract value, competitive win rates, adoption, and retention.

Design, develop, and deploy machine learning and LLM-based models for credit decisioning and fraud across the customer lifecycle. Build explainability, fairness, and responsible-AI tooling, collaborate with risk, governance, and engineering teams, and operationalize models with monitoring, drift detection, and auditability.

The Senior AI ML Engineer will design and implement AI/ML systems for healthcare, manage projects from ideation to release, and mentor engineers. Responsibilities include delivering quality code, driving architectural decisions, and collaborating with cross-functional teams.

Owns LSF cluster operations for EDA workloads, including job scheduling, incident troubleshooting, infrastructure automation, and AI-powered developer support tooling. The role serves as the team’s LSF authority, drives measurable efficiency improvements, supports VDI and compute environments, and contributes to AWS cloud integration and infrastructure architecture within Ericsson’s silicon design operations.

Build and operate production AI applications and shared platform capabilities using AWS Bedrock and Python. Develop AI agents, MCP servers, APIs, and distributed services; integrate AI into existing applications; evaluate retrieval, tool-use, orchestration, and model-performance approaches; and ensure reliability, security, testing, observability, and maintainability. Partner with technical and business stakeholders to identify workflow improvements, influence architecture and engineering standards, support delivery planning, and mentor less experienced engineers.

Lead AI strategy and technical execution for dating recommendation systems, driving real-time personalized recommendations and Generative AI integration. Own cross-functional alignment, responsible AI, team scaling, budget, and external representation while ensuring scalable delivery, evaluation, and organizational development.

Lead a dry etch manufacturing development team across shifts to drive process development, wafer flow, tool readiness, and execution metrics. Coach engineers, remove barriers, partner with cross-functional teams to resolve issues, and improve systems to increase development throughput.

Lead Legora's cloud and AI partner strategy and relationships, build and run co-sell motions, manage marketplace presence, develop joint GTM plans, drive partner-sourced pipeline into sales, and feed partner insights into Product and GTM.

Design and deploy AI-native marketing workflows and agents, own end-to-end delivery from scoping to integration, evaluate frontier models and agent frameworks, build integrations into the marketing data stack, and evangelize AI adoption across marketing teams.

Provide legal support for technology transactions across the firm: draft, structure, and negotiate tech agreements (SaaS, cloud, AI, data, IP), lead diligence for investments/acquisitions, advise on regulatory and outsourcing matters, coordinate cross-functional stakeholders, and manage multiple complex matters while delivering clear legal advice to business and senior executives.

Lead commercial structuring and field pricing execution for large, complex enterprise deals. Partner with sales, pricing strategy, and value management to design repeatable programs, drive pricing execution, analyze deal profitability, and coach account teams to scale high-growth outcomes.

Lead enterprise AI strategy and governance, design end-to-end economic and unit cost models for AI workloads, partner across Technology/Finance/Business to drive value realization, assess risk and policy, and scale GenAI/LLM and advanced analytics solutions companywide.

Lead enterprise adoption and scaling of AI and advanced analytics by designing AI frameworks, financial governance, unit economics, and risk-aligned policies. Partner with Technology, Finance, Product, Risk, and LOBs to build ROI-driven models for cloud and AI platform consumption, advise on GenAI/LLM architectures, and drive companywide AI strategy and implementation.

Leads enterprise AI-first technology delivery transformation at MetLife. Defines operating models, roadmaps, governance, controls, adoption strategies, delivery metrics, and readiness practices for agentic software development. Partners with CIO organizations, DevSecOps, Platform Engineering, Architecture, Risk, Audit, and Legal to implement traceable, reviewable AI-assisted workflows in a regulated environment. Advises senior leaders, resolves cross-organizational ambiguity, and drives initiatives through measurable adoption and business value realization.

Leads the design and implementation of MetLife’s AI-first delivery practice. Runs AI delivery pilots, develops context libraries and agentic coding patterns, establishes spec-driven development and review standards, creates reusable enablement assets, and enables teams across GitHub Enterprise and CI/CD tooling. Partners with DevSecOps, Platform Engineering, Architecture, and Learning & Development to address toolchain gaps, measure engineering effectiveness, build practitioner networks, and scale adoption in a governed enterprise environment.

Designs and maintains MetLife Global Technology’s AI-first operating model, including team archetypes, role and accountability frameworks, human-agent governance, lifecycle architecture, decision rights, and transition plans. Collaborates with technology, risk, audit, architecture, and delivery leaders to create standards and reference artifacts that support adoption across CIO organizations. The role also facilitates implementation, incorporates field feedback, and evaluates operating-model effectiveness in regulated technology environments.