Top Artificial Intelligence Jobs

Build and operate autonomous AI agents for go-to-market workflows including inbound, outbound, pipeline management, and customer engagement. Develop evaluation frameworks, observability, human oversight, and ROI measurement for production agents. Ship MCP servers, agent skills, and web applications integrating CRM and revenue systems. Set technical direction, architect shared codebases, collaborate with sellers and RevOps, and advance best practices for LLM agents and evaluations.

Lead the architecture and development of advanced analytics, data products, predictive models, anomaly detection systems, and GenAI solutions using Engineering and Supply Chain data. Translate stakeholder needs into scalable technical solutions, manage analytics and AI roadmaps, collaborate on production deployments, and integrate solutions into business processes. Ensure data quality, governance, security, explainability, bias detection, and responsible AI practices while influencing cross-functional stakeholders and driving adoption.

Lead Parallel’s AI-native sales team while carrying an individual pipeline, running discovery, and closing technical buyer cycles. Build sales playbooks, improve rep ramp and coaching, recruit talent, support segmentation, and scale the team. Own pipeline coverage, ramp time, and win rates while selling to companies building with AI agents and LLMs.

Design and lead development of a high-performance, multi-cloud AI platform and context layer for agentic workflows. Own the data lakehouse roadmap, context retrieval infrastructure, agent orchestration interfaces, MCP interoperability, HITL safeguards, and AI-native operational capabilities. Establish engineering standards, CI/CD, GitOps, infrastructure-as-code, observability, and cloud governance practices while guiding architecture decisions across teams and optimizing performance, security, and cost.

Serves as the division’s senior AI advisor, leading the integration of generative AI, LLMs, machine learning, and intelligent data-processing capabilities into mission-critical defense software. Develops AI pipelines, dashboards, prototypes, developer tooling, and AI-augmented engineering standards. Evaluates AI-generated code for security and reliability, mentors engineering teams, supports secure classified deployments, and identifies lifecycle automation opportunities. The role is onsite in Arlington, Mount Laurel, or San Diego, with travel up to 10%.

Architect and lead the delivery of scalable multi-cloud AI platforms, agentic workflows, API-driven applications, event-based systems, and Lakehouse architectures across Azure and AWS. Establish API governance, multi-cloud LLM routing, observability, infrastructure provisioning, and end-to-end ML lifecycle practices. Guide RAG, vector search, ontology-driven systems, and traceable ML deployments while mentoring technical teams and supporting public-health mission needs.

Build production-grade AI and NLP systems that verify generated claims against scientific, clinical, regulatory, and reference materials. Responsibilities include hybrid evidence retrieval, claim decomposition, entailment and contradiction detection, confidence scoring, abstention logic, human review workflows, evaluation dataset creation, error analysis, and traceable citation-based decisions. Collaborate with Medical, Legal, Regulatory, and scientific stakeholders to deliver high-stakes, reproducible AI solutions.

Lead the vision, strategy, and execution of applied AI/ML initiatives for Faire’s two-sided marketplace. Build recommendation, retrieval, ranking, personalization, LTV, causal inference, and LLM-driven content models. Develop systems for marketplace quality, trust, policy enforcement, and user engagement. Lead cross-functional workstreams, mentor or manage scientists and analytics engineers, and improve growth, acquisition, incentives, and discovery experiences.

Lead product discovery and delivery for client engagements, partnering with stakeholders, designers, and engineers to define opportunities, prioritize backlogs, manage roadmaps, and deliver measurable outcomes. Serve as a trusted advisor to senior clients while guiding AI-enabled product development. Apply AI tools for productivity and demonstrate practical understanding of LLM systems, RAG, data grounding, hallucinations, latency, cost, and evaluation.

Drive the development and roadmap of HPC and AI/ML products for physics, plasma, high-energy-density simulations, and resilient materials discovery. Define features, requirements, positioning, and go-to-market strategy; collaborate with engineering and NVIDIA software teams; identify customers and partnerships; and represent products at workshops and conferences.

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.

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.

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.

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.

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.

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 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.