Top Artificial Intelligence Jobs

Lead rapid systems engineering and technology integration to develop, prototype, and field solutions for logistics, mission support, and contested operations across the Indo-Pacific. Evaluate emerging technologies, run demonstrations and experiments, perform trade studies and risk assessments, produce engineering documentation and briefings, and support transition to operational deployment within TS/SCI environments.

Lead Business Analyst partnering with wealth management stakeholders to define, document, and deliver technology solutions. Drive requirements, user stories, process models, UAT, and AI-enabled use cases while collaborating with product, engineering, QA, and data teams. Support production triage and improve processes using AI, automation, and analytics.

Apply machine learning and AI to structured and unstructured IoT and other datasets: clean and integrate data, develop and deploy user-facing models, create visualizations/dashboards or lightweight web apps, advise clients, lead teammates, and apply inferential statistics and research design. Requires TS/SCI with polygraph.

Lead and enforce quality, risk, safety, reliability, and maintainability for defense manufacturing programs. Drive audits, supplier quality, root-cause/corrective actions, configuration management, and AS9100/ISO compliance. Support technical reviews, contract compliance, predictive risk analysis, and Basis of Estimate documentation. Mentor staff and lead cross-functional problem resolution.

Leads the internal audit function, developing and executing COSO/IIA risk‑based audit plans, reporting to the Audit Committee, liaising with regulators and external auditors, managing anonymous reporting investigations, conducting quality assessments, overseeing audit budget, and developing an audit team to strengthen controls across operations, compliance, and finance.

Track and evaluate emerging LLM and AI research, assess models and techniques, build prototypes, and collaborate with engineers to deploy useful methods in client systems. Advise product direction, establish metrics for accuracy, reliability, and cost, and communicate research findings clearly. The role requires strong applied research, engineering, judgment, and communication skills in a fast-changing early-stage environment.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Lead product direction, roadmaps, priorities, standards, and success metrics for major areas of an AI stockbroking app. Guide product managers, product owners, designers, and engineers across multiple squads; balance user experience, data, engineering, business, regulatory, and operational needs; drive launch quality and continuous improvement; and build a high-velocity product culture focused on measurable user value.

Lead product direction and execution for major areas of an AI neobank app. Own roadmaps, priorities, product standards, success metrics, launch quality, and continuous improvement across multiple squads and financial services, including insurance, payments, savings, investing, and travel. Guide product managers, product owners, designers, and engineers while balancing regulatory, operational, user, technical, and business considerations.

Lead product direction, roadmaps, standards, and success metrics for major areas of KIRA’s AI Investing App. Guide product managers, product owners, designers, and engineers across multiple squads; balance user experience, data, engineering, regulatory, operational, and business considerations; drive launch quality and continuous improvement across financial services including investing, payments, savings, insurance, and travel.

Lead product direction and execution for major areas of KIRA’s AI Finance App. Own roadmaps, priorities, product standards, metrics, launch quality, and continuous improvement across multiple squads and financial services, including insurance, payments, savings, investing, and travel. Guide PMs, product owners, designers, and engineers while balancing regulatory, operational, UX, data, engineering, and business considerations.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.

Join a 12-month Orbit program to consult with customers, prototype AI/ML and LLM-based solutions, build Celonis data models with SQL and PQL, execute proofs-of-concept, present value to stakeholders, and collaborate with Product & Engineering to drive value realization.

Participate in a 12-month Orbit program to consult with customers, scope requirements, prototype AI/ML and LLM solutions on the Celonis platform, build data models using SQL and PQL, deliver proofs-of-concept and demos, quantify business value, and collaborate with sales, implementation, and product teams to drive value realization.

Design and build end-to-end software primitives for AI infrastructure: domain modeling of power/datacenters/chips, authorization, audit, orchestration, vendor data ingestion (KMZ/BIM/Revit), and agent integration to enable safe, auditable agent operators. Ship production-quality systems and work across ambiguity to turn unstructured data into durable, composable entities.

Lead enterprise SharePoint and Microsoft 365 architecture, modernization, migration, governance, security, and platform strategy in a regulated financial-services environment. Design Power Platform workflows and RPA solutions, migrate automations to Power Automate, and embed LLMs, RAG, agents, bots, and responsible AI capabilities into enterprise collaboration systems. Conduct business analysis, advise stakeholders, oversee architecture reviews, and mentor distributed delivery teams.

Lead design and build of New York Life's enterprise AI platform and agentic solutions. Work across cloud-native infrastructure, model and agent lifecycle, RAG knowledge systems, multi-agent orchestration, MLOps/LLMOps, governance, CI/CD, observability, and production operations. Partner with security, legal, and architecture to embed responsible AI and deliver production-ready, compliant AI systems.

Work on statistical and machine learning solutions and generative AI (LLMs) projects. Translate business questions into models, perform exploratory analysis, feature engineering, model validation, and communicate results to technical and non-technical stakeholders throughout an 11-week summer internship.

Develop, train, and deploy machine learning and generative AI models in production. Focus on engineering aspects: scalable software, MLOps/DevOps integration, CI/CD pipelines, monitoring (e.g., New Relic), and automation frameworks to ensure model reliability and performance.

Participate in projects to improve and standardize technology operations. Collaborate with cross-functional teams to redesign processes, document improvements, create dashboards and reports, analyze data trends, and identify automation opportunities to increase operational effectiveness and adoption of shared tools and practices.

Develop and maintain cybersecurity solutions; monitor and analyze security events; investigate potential threats; strengthen controls across cloud and on‑prem environments; use security tools, automation, and threat intelligence; support secure code/configuration changes and vulnerability remediation; collaborate with engineering and infrastructure teams and leverage AI-powered technologies for detection and response.

Lead and build a small senior AI engineering team to design, build, and operate a governed enterprise AI platform and agentic AI solutions. Define architecture, roadmap, and governance; lead cloud and runtime architecture on Google Cloud; mentor engineers; partner with Security, Risk, and business stakeholders; implement AI FinOps, evaluation, and responsible AI controls; and deliver production-grade generative AI, retrieval, memory, orchestration, and agent lifecycle capabilities.

Lead design and implementation of the enterprise AI platform, defining reference architectures, reusable patterns, and complex services (lifecycle registries, control plane, memory, model abstraction). Build agentic AI capabilities, ensure secure governed deployments, drive portability and engineering standards, review designs, mentor senior engineers, and deliver production-grade AI systems across cloud-native infrastructure.

Design, develop, and enhance secure, scalable software applications using modern programming languages and cloud-native technologies. Collaborate with product, design, and engineering across the SDLC, apply Agile and DevOps practices, and leverage automation and AI-driven tools to improve productivity and code quality.