Top Artificial Intelligence Jobs

Provides strategic and operational leadership for nursing informatics and digital health initiatives across a health system. Partners with clinical, nursing, and IT leaders on EHR optimization, technology evaluation, digital transformation, virtual care, data governance, interoperability, cybersecurity, change management, and clinical education. Leads informatics work plans, mentors informatics staff, supports technology adoption, and evaluates emerging technologies such as AI, ambient documentation, and remote monitoring.

Designs, develops, deploys, and scales advanced AI and machine learning models and systems. Leads research initiatives, architecture decisions, technical design reviews, and continuous improvement efforts. Collaborates with data scientists, software engineers, and product managers, while mentoring engineers and communicating strategies to executive stakeholders. Applies data mining, modeling, NLP, deep learning, and statistical techniques to solve complex product and systems problems.

Lead technical sales for Deepgram’s Voice Agent platform, owning discovery, architecture, proofs of concept, technical evaluations, and customer production launches. Partner with account executives on enterprise deals, carry a growth quota, build demos and integrations, develop sales enablement, provide product feedback, and collaborate with telephony, contact center, and LLM partners. The role requires deep voice-agent expertise across telephony, orchestration, streaming pipelines, and speech and language models.

Lead the technical side of North American pre-sales cycles for global trade compliance software. Translate compliance requirements into tailored demonstrations, proof-of-concepts, solution architectures, RFP responses, and executive presentations. Partner with sales executives, configure and troubleshoot GTC platforms, address technical objections, and communicate AI applications in classification, screening, document processing, and duty optimization to enterprise compliance buyers.

The Senior Accountant oversees monthly, quarterly, and annual close processes; prepares financial statements, audit schedules, and reports; researches accounting policies; maintains internal controls; and identifies financial-system issues. The role supports treasury projects, leads process improvements, participates in department initiatives, reviews team members’ work, and trains colleagues. It requires effective communication with management, strong prioritization, attention to detail, and advanced Excel and Word skills.

Leads enterprise AI platform adoption by embedding with IT teams, building agentic workflows and integrations, improving software delivery and operations, and measuring engineering efficiency. Applies AI across the SDLC, DevSecOps, observability, security, governance, and legacy modernization. Translates field learnings into reusable platform capabilities, influences senior stakeholders, mentors engineers, and serves as a technical escalation point without direct management responsibility.

Lead applied AI research focused on information retrieval, RAG, agentic workflows, ranking, reasoning, and evaluation. Build prototypes, golden datasets, quality metrics, regression detection, and experimentation frameworks; investigate failure modes and validate improvements through rigorous testing. Collaborate with AI and software engineers to productionize successful approaches and stay current with research in RAG, agents, LLM evaluation, and relevance modeling.

Consults with stakeholders to identify capability gaps, define learning objectives, and develop program requirements. Coordinates curriculum alignment, stakeholder reviews, subject matter experts, and content governance. Facilitates leadership and manager development programs, conducts assessment debriefs, builds facilitator capability, and creates adoption and reinforcement plans. Measures behavior change, skills readiness, participant feedback, and business impact while managing projects to scope, timeline, and quality.

Own and improve Benefits Labs, an AI-powered system for intake, pilot governance, execution, measurement, evidence synthesis, and leadership recommendations. Build agents, automations, and workflows that scale operational change with minimal manual effort. Define success metrics, improve pilot quality, solve systemic process issues, coordinate cross-functional teams, influence decisions through data-driven narratives, coach Pilot Leads, and shape the future operating model for Benefits Labs.

Leads the technical vision, strategy, roadmap, and delivery of AI/ML capabilities across Cisco and Splunk products. Guides architecture, model development, production deployment, distributed training, and service integration while mentoring teams and influencing enterprise stakeholders. The role focuses on generative AI, large language models, deep learning, time series modeling, natural language processing, and scalable AI product development.

Define strategy, vision, roadmaps, and priorities for enterprise unstructured data products supporting analytics and AI. Partner with business, data, technology, risk, and control stakeholders to identify opportunities, build governed reusable data products, lead pilots, secure executive support, measure adoption and value, and ensure privacy, compliance, records management, and governance requirements are met.

Lead BHSI’s enterprise Master Data Management transformation, defining strategy, architecture, operating model, and roadmap. Own Party Data management, identity resolution, hierarchies, survivorship, governance, quality, and expansion into additional master and reference domains. Partner with underwriting, claims, finance, operations, and technology leaders to deliver trusted data products through APIs, streaming, and analytics platforms. Build the MDM team, manage vendors, guide platform implementation, and communicate outcomes to executives.

Own and support the firm’s AI legal applications and enterprise AI tools. Responsibilities include platform management, product roadmap tracking, user support, feature testing, adoption monitoring, governance, documentation, vendor coordination, integration evaluation, training, communications, and identifying opportunities to improve AI value and responsible adoption.

Lead a data science team developing scalable AI and machine learning products for Capital One’s mobile app. Build, fine-tune, evaluate, and operationalize NLP and large language models using technologies such as PyTorch, AWS, Hugging Face, and LangChain. Partner with engineering, product, and design teams to deliver customer-facing AI features, translate technical findings into business outcomes, and develop talent.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead governance and design reviews, contribute to technical roadmaps, and mentor senior AI engineering leaders.

Leads AI readiness and adoption across call center, back-office, and frontline operations. Designs learning, change management, communication, and reinforcement campaigns; consults with executives and HR leaders; integrates enterprise best practices; equips leaders with coaching resources; and develops measurement strategies using feedback and tool telemetry to improve AI adoption and operational performance.

Leads full-stack engineering initiatives involving distributed systems, cloud-native applications, AI agents, RAG pipelines, and real-time interfaces. Designs frontend and backend architectures using modern web frameworks, APIs, microservices, and enterprise integrations. Provides technical leadership across teams, establishes architecture and coding standards, mentors engineers, drives reliability and observability practices, and shapes platform roadmaps and technology strategy.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI performance governance, lead design reviews, define technical roadmaps, and mentor senior AI engineering talent.

Lead development of scalable AI and machine learning solutions for customer-facing applications. Build, fine-tune, evaluate, and validate NLP models and large language models, partnering with engineering, product, and design teams to operationalize them at scale. Research emerging generative AI technologies, deliver production-ready libraries and solutions, translate technical findings into business outcomes, and provide technical leadership across cross-functional teams.

Leads data science for AI Foundations, developing and deploying scalable NLP, machine learning, and generative AI solutions. Partners with data scientists, engineers, and product managers to train, evaluate, validate, and operationalize models serving millions of customers. Applies expertise in large language models, optimization, self-supervised learning, explainability, or RLHF while mentoring talent and translating technical work into business outcomes.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to strategic roadmaps, and mentor senior technical staff.

Design, develop, and deploy secure AI/ML and full-stack solutions for healthcare underwriting and benefits modernization. Build RAG pipelines, integrate LLMs and agentic AI capabilities, develop Python-based machine learning systems, and expose models through production APIs. Collaborate with engineering, architecture, product, data, and underwriting teams while ensuring model availability, security, performance, testing, monitoring, and responsible AI use.

Build and operate AI-enabled intelligence systems, knowledge repositories, retrieval systems, and decision-support workflows. Develop taxonomies, metadata standards, information models, prompts, agent instructions, and governance practices. Translate business and expert requirements into scalable technical workflows, integrate data and platforms, prototype new capabilities, and operationalize reliable solutions. Partner cross-functionally with Communications, Corporate Affairs, Data, Technology, and AI teams to improve information access, synthesis, consistency, and decision-making.

Leads enterprise AI solution architecture across healthcare, defining scalable patterns for generative AI, agentic AI, machine learning, intelligent automation, and cloud platforms. Establishes governance, responsible AI controls, reference architectures, and technology standards while guiding engineering teams through production deployments. Partners with executives, product leaders, data scientists, security, legal, and compliance teams; evaluates emerging technologies; prototypes AI solutions; and mentors architects and engineers. Requires deep experience with LLMs, RAG, AI agents, vector databases, healthcare systems, distributed architectures, and enterprise technology governance.

Owns product strategy and lifecycle for AI- and automation-enabled portfolio management capabilities within BlackRock’s Aladdin platform. The role partners with portfolio managers, investment professionals, engineering, and product teams to identify workflow improvements, define requirements, deliver scalable solutions, drive adoption, and measure business impact. It requires investment management knowledge, product judgment, technical fluency in software and AI, analytical problem-solving, and strong stakeholder communication.