Top Artificial Intelligence Jobs

Lead end-to-end AI idea funnel and portfolio for wholesale banking: identify, validate and prioritize AI initiatives; drive adoption, governance, and operating model; coordinate stakeholders across business, tech, data and risk; perform market research and prepare decision-making materials; support scaling flagship AI programs and accelerate measurable business impact.

Lead Responsible AI strategy, governance, and operationalization across Microsoft Security. Drive RAIS implementation via engineering systems and tooling, align MSec with enterprise RAI bodies, manage AI-related audits and regulatory inquiries, assess and mitigate AI risks, establish controls/metrics, and advise leadership to improve organizational maturity and compliance.

Support SEO strategy execution through keyword research, technical site audits, content gap analysis, page-level recommendations, off-site brand audits, integrated paid/organic analysis, and reporting using analytics and SEO tools. Collaborate with cross-functional teams and continually test new search and AI-driven approaches to improve client search visibility and performance.

Design, deploy, and maintain large-scale ML workflows and knowledge graph services. Build production-ready LLM and RAG-based search pipelines, data ingestion/preprocessing, feature stores, monitoring, and security. Collaborate with cross-functional teams, document systems, research advanced ML/NLP techniques, optimize knowledge graph algorithms, and mentor junior engineers.

Design, implement, and optimize AI algorithms and software for computer vision, NLP, recommendation systems and ASR. Develop, test, and maintain models and production-quality code, participate in code reviews and CI/CD workflows, and document algorithms and technical processes.

Design, build, and deploy production generative AI applications (agentic automation) using Node.js and TypeScript. Collaborate with product teams to apply LLMs, RAG, and fine-tuning techniques, manage model training/deployment infrastructure, iterate on real-world data, and monitor/evaluate AI performance to improve user experiences.

Design, prototype, and deploy edge AI models onto embedded hardware for secure, disconnected government environments. Build low-latency multi-node communication, quantize and compile multi-agent pipelines, develop asynchronous multimodal fusion and signal-processing algorithms, maintain HIL simulations, and lead ruggedized field assembly, calibration, and testing.

Build, optimize and support node-based generative AI workflows for production. Develop custom Python nodes and tools, integrate and fine-tune models (adapters, ControlNet, LoRA), optimize inference and memory, ensure temporal consistency for image/video generation, package reusable graph templates, and provide technical support and documentation to artists and production teams.

Create photorealistic characters, environments and motion assets using generative image/video AI and node-based workflows. Iterate, refine and finish production-ready assets with compositing, multimodal editing and collaboration with artists and production teams.

Own end-to-end customer rollouts: scope, configure, launch, and drive adoption of an AI-driven platform for home-service businesses. Manage a portfolio of customers, quantify ROI and operational impact, partner with forward-deployed engineers, and turn field learnings into scalable playbooks and product signals.

Own end-to-end customer rollouts: scope, configure, launch, and drive adoption of an AI-driven platform for home-service SMBs. Manage a portfolio of customers, quantify ROI and operational impact, partner with forward-deployed engineers, shape repeatable delivery playbooks, and feed product insights from the field.

Design, build, and productionize AI-powered applications across SpaceX. Own end-to-end lifecycle for LLM and agentic systems: prototype, evaluate, deploy, monitor, and iterate. Architect reliable, safe agentic workflows, advanced retrieval, and observability; collaborate with Product and Operations to deliver production-grade AI features.

Design, evaluate, deploy, and integrate LLM-based and agentic AI systems across SpaceX. Own end-to-end lifecycle from prototype to production, build evaluation and observability frameworks, architect reliable agentic and retrieval-enabled workflows, and collaborate with product, engineering, and operations teams.

Design, deploy, and maintain production AI systems (LLMs and agentic workflows) across SpaceX. Own end-to-end lifecycle from prototype to production, build evaluation and observability, architect reliable agentic and retrieval-based applications, and collaborate with Product, Engineering, and Operations.

Design, evaluate, deploy, and operate production-grade AI systems (LLMs and agentic workflows) across SpaceX. Own end-to-end lifecycle from prototype to production, build evaluation and observability, architect reliable and safe AI-powered applications, and partner with Product, Engineering, and Operations teams.

Design, evaluate, deploy, and integrate AI systems (LLMs and agentic systems) across SpaceX. Own end-to-end lifecycle from prototype to production, architect reliable agentic workflows and retrieval systems, build evaluation and observability frameworks, partner with product and operations, and deliver production-grade full-stack software to accelerate launches and Starlink operations.

Design, evaluate, deploy, and integrate LLM-based and agentic AI systems for production use across SpaceX. Own end-to-end AI lifecycle from prototype to production, build robust evaluation and observability frameworks, architect reliable agentic and retrieval-enabled workflows, and partner with product and operations to deliver safe, production-grade AI applications.

Design, build, and deploy production-grade AI systems (LLMs, agentic workflows) across SpaceX. Own end-to-end lifecycle: prototyping, evaluation, deployment, monitoring, and iteration, partnering with product and engineering to ensure reliability, safety, and observability.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Design and build AI-enabled robotics proofs of concept, then deploy and integrate them in customer sites. Responsibilities include camera placement and calibration, model deployment, I/O configuration, on-site troubleshooting, and customer training. Collaborate with sales and engineering teams, produce documentation and integration guides, and feed deployment learnings back into product development.

Build and integrate AI agents and automation workflows, extend full-stack apps with LLMs, manage data pipelines (Snowflake, Postgres), support server-side event tracking, analytics and dashboards, ensure secure HIPAA/SOC2-compliant handling of PII, and collaborate with leadership to deploy AI-driven marketing and compliance solutions.

Lead PJT's AI security and risk program: own AI risk framework and policies, evaluate vendors and internal AI deployments, perform AI threat modeling, embed AI security controls, monitor regulation and report to leadership, support audits, and deliver awareness training. Collaborate with Legal, Compliance, Procurement, and Technology to manage model, data, and third-party AI risks across the firm.

Build, operate, and secure the firm's AI infrastructure and developer tooling to enable Python-based AI applications. Implement Azure AI Foundry, integrate LLM APIs (Anthropic, Azure OpenAI), design data pipelines (ADF, Databricks), manage cloud infrastructure (IaC, Kubernetes), and enforce security, governance, and compliance for regulated financial environments.

Work with senior leadership to drive firm-wide AI adoption: study team workflows, identify high-leverage AI use cases, design and deliver training, recruit internal evangelists, evaluate tools/vendors, track adoption, and scale best practices across global offices.

Build and own core backend infrastructure for long-running agentic AI workflows, ensuring high reliability, observability, disaster recovery, secure execution sandboxes, HIPAA-compliant data handling, and production-grade IAM and secrets management.