Top Artificial Intelligence Jobs

Lead market and competitive intelligence for AI Assistants in travel: map ecosystem, monitor competitors and trends, run market sizing and segmentation, shape early business models and pricing, and produce executive-ready strategy recommendations to guide product and commercial decisions.

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.

Designs and operationalizes large language model fine-tuning workflows across supervised, preference-based, and reinforcement learning approaches. Responsibilities include dataset construction, evaluation methodology, distributed GPU training, failure recovery, production engineering, and collaboration with cross-functional teams. The engineer will ship impactful LLM solutions, participate in code and design reviews, mentor junior engineers, and contribute to reliable production pipelines.

Lead enterprise AI architecture and roadmapping for Department of War missions, including generative AI, LLMs, search optimization, cloud platforms, and model migrations. Modernize major data platforms across classified networks, design federated data governance and access-control solutions, and ensure interoperability across Palantir, Databricks, analytics, and ERP systems. Provide strategic direction, project planning, stakeholder communication, technical oversight, documentation, and performance tracking while supporting autonomous warfare and national security initiatives.

Build and scale an enterprise Knowledge Graph platform and semantic pipelines to power AI-driven features. Design graph models, semantic routers, vector/RAG workflows, cloud-native data pipelines, and AI developer tooling. Own feature delivery, ensure code quality, mentor engineers, and collaborate across product, ontology, and engineering teams.

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.

Transform ML prototypes into secure, scalable production systems on Databricks and mission platforms. Build and maintain ML pipelines, CI/CD, monitoring, drift detection, logging, and observability. Harden workflows to federal cybersecurity standards, troubleshoot production issues across cloud and on‑prem, and collaborate with data scientists and stakeholders to sustain operational ML capabilities.

Designs and scales secure, enterprise-grade AI and data platforms across Google Cloud, Azure, and AWS. The role develops internal developer platforms, CI/CD frameworks, infrastructure-as-code, deployment patterns, data pipelines, observability, governance, and security controls for production AI applications. Responsibilities include integrating enterprise manufacturing and supply chain data, enabling self-service AI capabilities, supporting cloud portability, partnering with technical and business stakeholders, mentoring engineers, and driving enterprise AI adoption.

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.

Own the technical direction for generative AI, multimodal, and voice-agent systems used in medical devices. Design deterministic agent architectures, safety guardrails, evaluation frameworks, and traceability processes for regulated clinical environments. Develop and optimize ASR, TTS, language, and multimodal models for edge and cloud deployment under latency and memory constraints. Lead model selection, validation, documentation, cross-functional alignment, code reviews, and mentoring.

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.

Own deployment and adoption of ARPA-H's AI assistant (GRACE): design agent/multi-agent workflows and prompts, train and onboard program teams, build reusable templates and playbooks, track usage and adoption, gather feedback, and translate needs into product requirements.

Supports the design, delivery, tuning, and monitoring of AI-enabled solutions for state government clients. Facilitates requirements sessions, translates policy and operational workflows into structured deliverables, designs evaluation test sets, analyzes AI performance using basic statistics, and iterates on prompts and instructions. Partners with technical teams on workflows, model tradeoffs, cost, explainability, integrations, and responsible AI practices while supporting cross-workstream delivery operations.

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.

Provide technical support to government teams developing and evaluating AI applications. Advise on LLM, RAG, agentic workflow, orchestration, architecture, integration, evaluation, reliability, observability, guardrails, and secure AI practices. Develop prototypes, technical guidance, recommendations, and stakeholder briefings while assessing emerging frameworks and mission-use-case suitability. Collaborate with engineers, product leads, mission users, and government leadership on operationally relevant AI solutions.

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 a strategic and operational force multiplier for the Edge CVP and leadership team. Translate priorities into execution plans, operating rhythms, decision forums, and accountability mechanisms. Lead talent planning, organizational health, executive communications, special projects, and cross-functional initiatives. Own OPEX, COGS, headcount planning, forecasting, investment tradeoffs, and spend tracking. Manage and develop business management and program management teams.

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.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

Owns end-to-end architecture for a major generative AI platform and shared AI services. Responsibilities include designing model integrations, APIs, data flows, orchestration, RAG and agentic workflows; defining architecture standards, integration contracts, and non-functional requirements; ensuring security, resilience, compliance, governance, observability, and scalability; and conducting architecture reviews and proof-of-concept assessments. The role partners with product, engineering, data science, cloud, security, and control teams to deliver production-ready enterprise AI systems.

Own the technical design and operation of an enterprise AI collaboration platform integrating Slack, Google Workspace, Glean, Claude, ChatGPT, and other systems. Build production AI agents, workflows, RAG solutions, APIs, connectors, and secure integrations. Establish standards for development, testing, deployment, governance, and security; administer platform access and configurations; troubleshoot complex issues; evaluate AI quality and safety; lead technical reviews; mentor contributors; and advise platform leadership.

The Enterprise AI Programs & Governance Specialist collaborates with various teams to ensure AI initiatives align with business strategy, oversees the intake and governance processes, and facilitates cross-functional alignment for effective AI program management.

Design and implement scalable, distributed, AI-driven SD-WAN and AIOps services. Develop real-time, fault-tolerant message/event-driven systems, write and test code, participate in design/code reviews, and drive end-to-end feature delivery and operability improvements.