Top Artificial Intelligence Jobs

Manages computing, AI, cybersecurity, and related research programs and projects. Develops strategies, budgets, schedules, and staffing plans; recruits expert committees; coordinates workshops and roundtables; synthesizes technical research; oversees publications and dissemination; manages staff; builds sponsor relationships; and supports fundraising. Requires strong technical communication, facilitation, project management, and independent judgment in complex scientific and policy environments.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Leads technical product strategy and multi-year roadmaps for transmission, distribution, renewable energy, and grid software solutions. Manages and scales product teams, drives AI/ML-enabled capabilities, collaborates with engineering and data science, engages utilities and regulators, supports technical sales, and oversees product performance, pricing, licensing, and monetization. Requires deep energy and utility domain expertise, T&D automation or SCADA experience, enterprise software leadership, and strong cross-functional stakeholder management.

Design and modernize enterprise-scale data platforms using Azure, Databricks, Microsoft Fabric, Synapse, and Spark. Lead data architecture strategy, modeling, integration, governance, metadata, lineage, security, lakehouse design, semantic layers, scalable pipelines, and AI/ML-ready architectures. Collaborate with senior business and technology stakeholders while remaining hands-on with technical design and implementation. Experience with GenAI, vector databases, and financial services is preferred.

Designs, operates, and scales Kubernetes-based data and AI infrastructure across public cloud environments. Builds platforms for Kafka, Spark, Flink, AI gateways, model serving, traffic management, observability, and self-service deployment. Partners with engineering teams to improve reliability, scalability, performance, cost efficiency, and operational simplicity of distributed systems.

Own the AI strategy and execution for Bullish and CoinDesk business teams. Build and deploy AI workflows, copilots, automation, and agentic solutions; identify and prioritize high-value use cases; establish self-service enablement through tools and training; define adoption, efficiency, and ROI KPIs; report outcomes to executives; and build the AI Enablement function and team from the ground up.

Own end-to-end analytics pipelines for AI usage, developer experience, engineering productivity, and spend. Instrument telemetry, build governed ETL workflows, define trusted metrics, and create executive dashboards and visual narratives. Analyze results, recommend improvements to AI ROI and developer experience, and communicate methodology to senior stakeholders. Collaborate across data engineering, applied AI, and data science while using AI coding agents to build, test, and maintain production data systems.

Leads full-stack application development for BNY’s AI Hub Engineering team. Responsibilities include designing, developing, testing, integrating, securing, modernizing, and maintaining scalable front-end and back-end systems. The role advises business and IT leaders, resolves complex technical issues, supports enterprise technology strategies, conducts code reviews, mentors engineers, manages technical debt, and presents technology solutions to stakeholders.

Lead design and implementation of AI and data governance frameworks, privacy guardrails, metadata standards, and compliance with GDPR/HIPAA/CCPA/NIST AI RMF. Monitor and audit models for bias, explainability, and transparency; partner with engineers, stewards, and leaders to embed ethical decision-making, set KPIs, run audits, and produce dashboards and reports to drive governance adoption.

Architects and leads production AI products, including chatbots, document processing, agentic workflows, and centralized AI platforms. Designs model routing, vector search, retrieval, context engineering, MCP integrations, and scalable cloud-native systems. Integrates AI with accounting systems, repositories, APIs, and enterprise technologies while ensuring monitoring, compliance, and observability. Provides technical leadership, establishes AI development standards, and mentors engineers.

Own strategy, roadmap, and delivery of agentic AI capabilities to transform end-to-end workflows across WCIB. Lead discovery, launch, and optimization of reusable, auditable AI agents; ensure human-in-the-loop controls, compliance with enterprise AI governance, and cross-functional alignment with Risk, Compliance, Legal, and Technology. Drive adoption, measurable business outcomes, and scale products from pilot to production.

Partners with business leaders to identify AI opportunities, define requirements, prioritize backlogs, and coordinate solutions from discovery through launch. Supports AI solution design, testing, governance, security and privacy reviews, rollout, training, adoption, and post-launch measurement. Monitors usage, quality, cost, and business value, recommending continuous improvements. Translates between business stakeholders and technical teams while communicating priorities, risks, progress, and outcomes within established frameworks.

Leads business intelligence, AI, predictive analytics, data governance, and data architecture for a pharmaceutical manufacturing site. Builds dashboards, analytics pipelines, KPI frameworks, and predictive models; governs data quality, lineage, GxP compliance, FDA 21 CFR Part 11, and ALCOA+ standards. Integrates SCADA, MES, LIMS, ERP, and IoT data while developing and mentoring a technical team. Tracks business value through improvements in yield, uptime, cycle time, quality, energy efficiency, and operational performance.

Lead the design and delivery of production AI systems, agentic workflows, and shared AI platform capabilities for loan origination and servicing. Responsibilities include model selection and routing, MCP and agent interfaces, RAG and context engineering, evaluation and observability, reliability practices, and scalable backend architecture. The role operates independently on complex initiatives, establishes technical patterns, makes platform decisions, and mentors engineers.

Leads AI product operations for utility customers by identifying and validating agentic use cases, directing forward-deployed engineering strategy, and scaling customer co-creation. Partners with product, engineering, customers, Google, NVIDIA, and other AI partners to deliver monetizable capabilities. Builds reusable AI assets, evaluation systems, and learning loops while supporting product marketing and sales enablement. The role requires hands-on AI fluency, technical judgment, commercial perspective, and experience leading customer-facing technical teams.

Own the full product lifecycle for Pharos’s mature PrintOps product, including understanding legacy behavior, setting roadmap priorities, guiding expansion into two new markets, engaging enterprise customers, translating product direction into engineering work, and aligning Sales, Support, Customer Success, Services, and Marketing. The role requires practical use of AI for discovery, research, synthesis, documentation, and epic development.

Advise life sciences and biotech startups on building products and research workflows with Claude. Partner with engineering and scientific teams through architecture reviews, pair programming, prototyping, evaluations, and technical documentation. Develop reference implementations, MCP servers, agent skills, benchmarks, and scalable architectures for scientific applications. Share deployment insights with Anthropic product and research teams, create developer content, engage the startup ecosystem, and travel to customer sites for workshops and implementation support.

Serve as a technical advisor to physical AI startups across robotics, aerospace, semiconductors, manufacturing, and civil infrastructure. Partner with software and hardware teams on Claude integrations, agent architectures, prototypes, evaluation frameworks, reference implementations, and technical documentation. Identify recurring engineering challenges and develop scalable solutions, while supporting workshops, hackathons, events, and customer-site engagements.

Leads the design, delivery, and operation of enterprise AI platforms and LLM-based systems. Responsibilities include AI architecture, RAG and fine-tuning, model evaluation, platform development, cloud services, governance, buy-versus-build decisions, cross-team delivery, stakeholder communication, and engineering team leadership and mentorship.

Leads a portfolio of strategic transformation initiatives across AI, private markets risk management, operating model redesign, and cross-functional innovation. Responsibilities include program governance, prioritization, milestone and dependency tracking, executive reporting, stakeholder influence, AI adoption, governance framework development, operating model design, and value realization. The role partners with senior leaders across risk, technology, investment, and corporate functions to deliver complex, multi-year initiatives.

Develops and evaluates AI and machine-learning models for intelligence collection prioritization, tasking automation, resource optimization, and mission planning. Responsibilities include data preparation, predictive modeling, validation, monitoring, explainability, responsible AI, and collaboration with software engineers on deployment. The role briefs technical and mission stakeholders and requires work in a DIA SCIF supporting secure DoD and Intelligence Community operations.

Leads enterprise sales for Microsoft Azure cloud and AI platforms. Develops customer-specific sales strategies, qualifies and closes complex opportunities, manages pipelines and forecasts, coordinates partners and internal teams, and advises stakeholders on AI-driven cloud transformation. Uses market intelligence, consumption data, and predictive analytics to drive adoption, expansion, renewals, and customer value. Requires strong consultative selling, enterprise relationship management, and knowledge of Azure infrastructure, data, and AI application platforms.

Build and maintain premium mobile features, subscription and in-app purchase flows, paywalls, pricing experiences, and advertising functionality for Life360’s consumer app. Own feature quality, performance, reliability, experimentation, and incident response at 100M+ user scale. Collaborate with product, design, data science, and analytics teams; use AI coding agents extensively; contribute to architecture and code reviews; mentor engineers; and help evolve AI-native engineering practices.

Supports and advances institutional AI infrastructure, automation platforms, agents, copilots, bots, cloud services, and integrations. Designs, configures, tests, secures, documents, and maintains AI-enabled systems while evaluating solutions for privacy, accessibility, compliance, and risk. The role also contributes to AI governance, lifecycle management, technical documentation, stakeholder collaboration, training, and strategic planning across the university.

Leads customer co-creation and rapid validation of agentic AI capabilities for utility customers. Partners with product, engineering, forward-deployed teams, and Google and NVIDIA to define deployment strategies, deliver customer value, and build reusable AI assets. Translates customer evidence and AI advances into scalable product capabilities, marketing, sales enablement, and strategic partnerships while influencing platform and architecture decisions.