Top Artificial Intelligence Jobs

Supports technical documentation updates by identifying placeholders and missing information, researching software manuals with ChatGPT and approved resources, and incorporating verified information into documents. Responsibilities include checking AI-assisted results against source materials, preserving accuracy and formatting, proofreading, tracking completed work, escalating questions, and following document-control, confidentiality, and approved AI-use requirements.

Serves as the senior AI authority for defense software engineering, leading generative AI, LLM, machine learning, NLP, and AI automation adoption. Integrates AI models into mission-critical systems, develops intelligent data pipelines and AI-enabled interfaces, evaluates developer tooling, establishes secure AI engineering standards, and mentors engineering teams. The role also prototypes AI use cases, supports responsible AI governance, and collaborates across engineering, security, compliance, program management, and systems teams.

Develop and execute traditional and AI-search SEO strategies to increase organic visibility, local presence, qualified leads, and signed cases. Responsibilities include technical, on-page, off-page, local, and content optimization; keyword and entity research; structured data implementation; backlink and competitor analysis; AI citation monitoring; performance reporting; and resolving crawlability, indexing, speed, and Core Web Vitals issues. Collaborate with writers, attorneys, developers, and marketing leaders while staying current on search trends and legal advertising requirements.

Leads robotics and AI engineering teams developing production-grade robotic systems, real-time computer vision models, ROS/ROS 2 control pipelines, and edge-deployed deep learning solutions. Owns vision pipeline architecture, object detection, segmentation, tracking, 3D pose estimation, data annotation, model training, MLOps, system integration, and performance targets. Also provides technical architecture guidance and manages engineering team capabilities.

Designs and delivers enterprise generative and agentic AI applications using RAG, Graph RAG, knowledge graphs, context engineering, and multi-agent orchestration. The role integrates foundation-model APIs, tools, and data through MCP and A2A, while ensuring production scalability, observability, governance, privacy, guardrails, and reliability. Responsibilities also include real-time AI development, responsible AI compliance, mentoring junior developers, code reviews, and cross-functional collaboration.

Develop and maintain high-quality technical manuals, guides, specifications, and related documentation. Lead information sessions with subject matter experts, align content with user needs and product requirements, improve existing materials, manage documentation projects, incorporate usability-testing feedback, and coordinate with design teams on visuals and diagrams. Apply documentation tools, industry standards, and best practices to improve clarity, usability, and content quality.

Leads the design, development, testing, deployment, governance, and continuous improvement of enterprise AI products and integrated data platforms. Oversees SAP Joule, SAP BDC, AI-enabled applications, decision intelligence, platform architecture, responsible AI, compliance, and DevOps practices. Manages multidisciplinary engineering, data, and AI teams, technology portfolios, budgets, risks, stakeholder alignment, and workforce enablement across a large healthcare transformation program.

Build and support practical AI solutions across GE Appliances by mapping business workflows, prioritizing use cases, developing prototypes and automations, testing with users, and creating reusable guides and playbooks. Lead workshops, office hours, and reskilling efforts to drive AI adoption. Collaborate with Digital Technology and AI Platform teams to align solutions with approved tools, while tracking adoption, time savings, quality improvements, and business impact.

Own Tribe’s corporate security, compliance, AI governance, and client-facing security programs. Lead SOC 2 and future certification efforts, enterprise security reviews, risk management, security controls, incident response, vendor risk, and Trust Center operations. Establish governance for AI agents, assistants, model providers, and subprocessors while partnering with engineering on architecture reviews, threat modeling, secure SDLC, data flows, and production readiness. Translate customer requirements into scalable security controls and operating processes.

Design, train, and interpret foundation models for multimodal sensor data. Develop representation learning, multimodal alignment, self-supervised learning, and model adaptation methods. Diagnose limitations and failure modes, design experiments, and collaborate with engineering and product teams to translate research into production systems.

Design and test challenging prompts based on complex professional or personal workflows. Identify AI failure points by iterating prompts in ChatGPT, then create a comprehensive grading rubric for evaluating model outputs. Participants must share their screen, camera, and microphone during the recorded trial and submit the prompt, failure notes, rubric, and generated output file. Successful participants may be considered for ongoing AI evaluation work.

Design and implement large-scale, AI-driven SD-WAN and AIOps systems. Develop fault-tolerant, real-time, message/event-driven services in C++/Python/Go. Drive feature design, code and design reviews, testing, debugging, optimization, and end-to-end delivery while collaborating with product teams.

Analyze campaign performance across Google Ads, Meta, LinkedIn, and internal systems; diagnose underperforming campaigns; build Excel and Sheets models; audit campaigns across many locations; identify performance patterns; clean and analyze data; and help implement AI-enabled analytical workflows. The role emphasizes quantitative reasoning, basic code literacy, clear communication, and independent, detail-oriented work.

Build and operate agentic AI products end to end, including LangGraph-based workflows, LLM evaluation systems, React interfaces, Java Spring Boot and Python services, REST APIs, iOS features, and distributed cloud infrastructure. Lead system design, testing, performance improvements, and engineering standards while mentoring junior engineers. Collaborate with product and design teams to deliver reliable multi-tenant applications and continuously improve agent quality through regression testing, trajectory scoring, and evaluation-driven development.

Builds and evaluates AI prototypes, workflows, automations, agents, and lightweight applications for enterprise business problems. Partners across functions to identify AI opportunities, redesign workflows, and improve productivity and decision-making. Creates reusable AI playbooks and learning assets, coaches teams on adoption and responsible AI, and collaborates with technology teams on approved tools, data practices, and production pathways. Tracks business impact, adoption, capability growth, and responsible AI practices.

Serve as the strategic and technical AI advisor for a defense engineering division. Lead AI and LLM integration into mission-critical software, develop NLP pipelines and AI-enabled dashboards, prototype emerging developer tools, establish AI-augmented engineering standards, and mentor teams on generative AI workflows. Evaluate AI-generated code for security, correctness, and performance while supporting responsible AI, secure SDLC practices, and deployment in sensitive environments. Collaborate with engineering, security, compliance, program management, and chief engineering teams.

Leads strategy, roadmaps, and delivery for AI/ML and Generative AI products supporting commercial, medical, and government affairs. Creates product requirements, manages backlogs, partners with technical and business teams, builds ROI cases, drives experimentation and launches, and oversees responsible AI governance, scalability, compliance, and post-launch optimization. Requires strong executive communication and experience managing enterprise AI initiatives.

Lead client engagements that identify, prioritize, and operationalize high-value AI opportunities. Responsibilities include process and value-stream mapping, stakeholder workshops, business-case and ROI development, AI platform evaluation, strategy and roadmap creation, KPI definition, senior-stakeholder advising, proposal support, and mentoring. The role bridges business and technology to guide AI investments, workflow improvements, automation, and organizational capability development.

Build and maintain mathematical optimization models for network planning, capacity allocation, routing, and freight operations. Develop production AI applications for process automation, document extraction, and decision support. Establish evaluation, validation, monitoring, and regression-testing frameworks for optimization and AI systems. Identify new business opportunities, prototype solutions, quantify impact, and scale successful initiatives. Partner with operations, pricing, sales, finance, engineering, and leadership to communicate trade-offs and deliver reliable analytical systems.

Serve as the first point of contact for customer questions, reviewing AI-generated responses, triaging urgent payroll issues, and managing queue resolution. Create help content, identify recurring product problems, and partner with product and engineering on fixes. Over time, own AI support agents and knowledge systems while using AI coding tools to ship small product improvements. This is an in-person, high-ownership role based in New York City.

The AVP, Business Analytics & AI leads the execution of data, analytics, and AI strategies, driving measurable business outcomes and ensuring governance of data products and AI operations while partnering with business and technology leaders.

Leads enterprise AI platform strategy, roadmap, governance, investment, and delivery across engineering, architecture, product, security, legal, risk, finance, and operations. Oversees complex platform programs, dependencies, KPIs, costs, scalability, ethical AI practices, and organizational adoption. Evaluates emerging AI technologies, manages ecosystem partnerships, and advises executives while translating technical strategy into measurable business outcomes.

Own the post-sale lifecycle for enterprise customers, leading onboarding, adoption, value realization, expansion readiness, workflow configuration, integrations, platform tuning, and customer health management. Partner with Product, Engineering, and Sales to coordinate milestones, resolve escalations, deliver technical enablement, communicate with executive stakeholders, and translate customer insights into product improvements. Identify opportunities to expand usage across cybersecurity workflows while driving measurable business outcomes.

Designs and supports enterprise observability platforms for monitoring, logging, tracing, alerting, and service health. Automates incident response, remediation, runbooks, and self-healing workflows using scripting, orchestration, and infrastructure-as-code. Develops AI-powered anomaly detection, event correlation, predictive monitoring, and Agentic AI integrations to improve troubleshooting and reduce MTTR. Collaborates with SRE, cloud, platform, application, and operations teams while participating in on-call and major incident support.

Lead the architecture and hands-on delivery of production AI systems using agentic frameworks, RAG, LLM orchestration, document intelligence, knowledge graphs, and evaluation pipelines. Manage and mentor 4–8 engineers, direct architecture and delivery, build Python and Java services, establish observability and CI/CD, and integrate AI platforms with enterprise systems. The role owns technical strategy for multi-agent workflows, vector retrieval, prompt engineering, validation, and AI-assisted developer tooling.