Top Artificial Intelligence Jobs

Designs, develops, and deploys agentic AI systems for cybersecurity, including vulnerability analysis, threat detection, alert triage, secure code generation, and remediation recommendations. Builds RAG pipelines, LLM integrations, APIs, microservices, and cloud-based platforms. Implements AI guardrails, threat modeling, human-in-the-loop governance, adversarial testing, explainability, and performance optimization while integrating systems with SIEM, SOAR, vulnerability management, and endpoint security tools.

Lead enterprise AI software architecture for distributed, mission-critical systems in classified, air-gapped, multi-cloud, and tactical edge environments. Design LLM platforms, multi-agent systems, RAG solutions, inference pipelines, hardware-optimized execution, zero-trust security, and DevSecOps architectures. Serve as the principal technical authority, establish engineering standards, guide roadmaps, lead research initiatives, and collaborate with executive and intelligence-community stakeholders.

Build and operate production Python services, AI-enabled workflows, RAG systems, multi-agent applications, and security automation platforms. Develop integrations, scalable data pipelines, code-review tools, and AI evaluation frameworks while managing reliability, latency, security, and cost. Partner with engineering and security stakeholders to identify opportunities and deliver maintainable solutions from concept through production.

Leads security control assessments for DoD AI and LLM systems across AWS, DevSecOps pipelines, containers, microservices, and multi-classification environments. Develops RMF assessment artifacts, evaluates cloud and AI-specific risks, supports continuous monitoring and architecture reviews, and coordinates ATO recommendations with Authorizing Officials and engineering teams. The role also contributes to penetration testing oversight, AI security frameworks, cATO methodologies, and secure design assessments for agentic and RAG-based applications.

Develop and maintain bespoke enterprise applications and websites using Microsoft technologies and Azure. Collaborate with business, project management, and IT teams to deliver application capabilities, automate development processes, support monitoring, and manage upgrades and integrations. Apply LLM prompting, evaluation, RAG, grounding, and agentic AI techniques to translate business requirements into measurable AI solutions.

Own and improve ClickUp’s voice AI systems, including real-time speech-to-text, audio processing, context-aware transcription, LLM post-processing, mention resolution, formatting, and voice-to-action workflows. Evaluate and integrate ASR models for accuracy, latency, and cost, while collaborating across product and platform teams to deliver voice and multimodal assistant features across desktop, mobile, web, and browser extension products.

Build and ship production AI-enabled product features for transportation agencies. Partner with product, design, customer success, and data science teams to identify valuable AI applications, implement full-stack capabilities, evaluate LLM correctness and grounding, enforce authorization and customer isolation, and operate features in production. Select appropriate techniques such as prompting, retrieval, tool use, text-to-SQL, fine-tuning, or conventional software while managing reliability, latency, cost, monitoring, and incident response.

Own and scale production LLM features and browser automation for a consumer subscription product. Responsibilities include prompt systems, structured outputs, Playwright and Browserbase automation, workflow orchestration, evaluation pipelines, observability, model experimentation, cost and reliability optimization, and production incident ownership. The role uses a modern TypeScript stack with Cloudflare Workers, Next.js, Postgres, Hatchet, and related AI tooling.

Design and build production-grade agentic AI systems and RAG pipelines, partner with business leaders to automate complex workflows, prototype across AI platforms, integrate models, APIs and data sources, and drive enterprise adoption and scalable solutions.

Hands-on QA Engineer responsible for exploratory, functional, regression, API, security, accessibility, integration, and AI-feature testing across a legal SaaS platform. The role validates document retrieval and delivery workflows, investigates defects, uses SQL, logs, dashboards, and simulators for backend verification, and creates targeted automated checks with tools such as Playwright. Responsibilities also include testing authentication, permissions, LLM behavior, prompt injection, third-party integrations, and production issues while collaborating with engineering and product teams.

Lead the strategy, roadmap, and delivery of Grata’s AI-native search and discovery engine for private-market dealmakers. Own semantic search, ranking, recommendations, and LLM/RAG workflows, partnering with design, engineering, data science, QA, marketing, commercial teams, and executives. Use customer research, data analysis, and measurable KPIs to improve relevance, quality, latency, cost, reliability, and trust while translating complex AI concepts into clear product requirements and outcomes.

Build, deploy, and operate production AI systems including LLM applications, RAG pipelines, agents, APIs, data workflows, and containerized services. Implement governance, security, observability, evaluation, and cost controls for enterprise AI. Collaborate across engineering, product, data science, compliance, and business teams; support architecture decisions, incident response, CI/CD, and mentoring. The role focuses on reliable AI capabilities for insurance underwriting, claims, member experience, and risk management.

Design and implement AI-powered image and video generation workflows for creative production. Collaborate with artists to integrate open-source generative models, 3D inputs, fine-tuned diffusion models, LoRAs, and ComfyUI features. Develop custom nodes and Python utilities for data processing and workflow automation while translating AI research into practical applications for film, commercial, and immersive experiences.

Build and operate production AI agent systems using Python, LangChain, LangGraph, and Amazon AgentCore. Develop agent tools, Snowflake integrations, retrieval workflows, structured outputs, evaluations, guardrails, and observability. Deploy, monitor, and improve reliable LLM-powered features while collaborating with data engineers. The role requires production Python experience, hands-on LLM development, evaluation framework design, and familiarity with modern AI tooling. Periodic travel may be required.

Build and scale production generative and agentic AI systems supporting the software delivery lifecycle. Develop multi-agent workflows with Google ADK, Gemini, and Claude; integrate enterprise tools such as JIRA and GitHub; create RAG pipelines and evaluation harnesses; and build asynchronous Python and FastAPI services backed by PostgreSQL. Apply testing, observability, CI/CD, containerization, secure coding, and AI guardrails to reliable enterprise deployments.

Design, build, and deploy enterprise-grade Generative AI and agentic AI platforms. Responsibilities include developing LLM, RAG, Graph RAG, vector search, multi-agent, MCP, data, backend, full-stack, cloud, inference, observability, LLMOps, security, and governance solutions. The role requires production deployment across AWS, Azure, and GCP, optimization for accuracy, latency, cost, scalability, and reliability, and collaboration with cross-functional technical and business teams.

Develop and maintain full-stack Java applications using Java EE, RESTful microservices, Angular, TypeScript, Node.js, SQL, PL/SQL, and Oracle. Use AI coding assistants such as GitHub Copilot for code generation, debugging, optimization, and prompt-driven development. Validate AI-generated code for accuracy, performance, and security while applying JVM performance profiling. Collaborate on production-ready solutions, with familiarity in CI/CD, DevOps, Unix scripting, Docker, caching, and SAML preferred.

Designs, deploys, and secures AI agents for enterprise risk and security operations. The role manages AI governance, Microsoft security and compliance controls, data protection, DLP, insider risk, access controls, monitoring, incident response, and SIEM/XDR integrations. It also leads security architecture, automation, AI risk evaluation, cross-functional initiatives, and mentoring while supporting secure adoption of Microsoft Copilot and Azure AI platforms.

Develop production-grade AI agents and reasoning workflows in Python, including memory management, sub-agent delegation, MCP server integrations, resilient execution pipelines, guardrails, testing, observability, and enterprise system connectivity. Collaborate with business and technology teams, coach junior engineers, review code, influence architecture, and ensure compliance, security, and quality standards across agile delivery squads.

Develop and deliver native iOS features for MyFitnessPal’s AI-powered food logging experience. Responsibilities include implementing user interfaces, optimizing performance and network communications, building tested and secure solutions, collaborating with product and design teams, researching technical approaches, mentoring developers, and contributing to deployment and engineering best practices.

Lead compliance advisory for Mercury’s AI and agentic banking products across discovery, design, launch, and iteration. Identify and mitigate risks involving AI outputs, agent permissions, consent, explainability, disparate impact, data leakage, prompt injection, third-party agents, and auditability. Define AI product compliance standards, documentation, logging, escalation, and human-review requirements. Monitor regulatory developments, support remediation and audits, manage concurrent workstreams, advise executives and partner banks, and mentor compliance professionals.

Build and operate AI agent capabilities and full-stack experiences for an internal enterprise intelligence platform. Develop retrieval, RAG, orchestration, tool integrations, prompts, evaluations, observability, APIs, and user interfaces for search, chat, and workflow automation. Collaborate with business stakeholders and data engineering teams while applying secure, reliable software engineering practices, including testing, CI/CD, monitoring, and on-call ownership.

Design and build production-grade AI agent and RAG systems for enterprise network automation. Responsibilities include agent orchestration, tool execution, memory, safety controls, LLM fine-tuning, evaluation, observability, reliability engineering, performance optimization, and production operations. The role also prototypes emerging AI technologies, diagnoses complex system failures, and collaborates across Engineering, Product, and QA to deliver scalable, secure AI capabilities.

Develop and ship software end to end using AI-assisted coding tools, while reviewing, testing, securing, and correcting generated code. Lead modernization work, evaluate AI tooling through measurable outcomes, establish guardrails, and share effective practices with engineering colleagues. The role requires strong software fundamentals, experience across legacy and modern stacks, production ownership, and independent judgment in ambiguous situations.

Assess and secure emerging AI technologies through architecture reviews, code reviews, penetration testing, threat modeling, and remediation guidance. Partner with engineering and business teams to establish secure development standards, research AI vulnerabilities, and build tools supporting secure AI use at scale. The role focuses on LLMs, agentic systems, RAG architectures, prompt injection, data exfiltration, privilege escalation, and model supply chain risks.