Top Artificial Intelligence Jobs

Leads AI engineering teams in designing, productionizing, and integrating enterprise AI, generative AI, automation, and MLOps solutions. Drives rapid delivery, responsible AI adoption, observability, security, and cross-functional automation initiatives across business functions. Partners with technology, business, architecture, and security teams to deliver scalable solutions using cloud, enterprise platforms, agent-based systems, LLMs, and retrieval technologies.

Build production agentic AI systems and internal automation for business teams. Prioritize high-impact workflows, develop multi-step LLM agents, integrate platform capabilities, implement human-in-the-loop controls, and establish reusable design patterns. Evaluate reliability, accuracy, speed, and failure modes while collaborating with nontechnical stakeholders. The role requires strong Python and backend engineering skills, production LLM experience, systems thinking, and the ability to operate independently in ambiguous problem spaces.

Design and build production agentic AI systems and internal automation workflows across business functions. Responsibilities include prioritizing high-impact automation, developing multi-step and multi-agent workflows, integrating agent platforms, implementing human-in-the-loop controls, improving reliability and observability, evaluating performance, and shaping reusable AI engineering patterns. The role requires collaboration with nontechnical stakeholders and ownership of ambiguous problems from definition through measurable business impact.

Leads the configuration, deployment, operation, governance, and security of enterprise AI platforms and agentic infrastructure. Manages models, agents, prompts, skills, MCP servers, integrations, access controls, monitoring, guardrails, lifecycle management, usage, and costs. Partners with infrastructure, development, security, compliance, and data teams to support HIPAA-aligned deployments, technical risk reviews, vendor evaluations, standards, runbooks, and AI architecture initiatives. Provides operational dashboards, technical governance, and after-hours support as required.

Design, develop, deploy, monitor, and support enterprise AI agents and Generative AI applications. Build RAG pipelines, agentic workflows, prompt-engineering solutions, LLM integrations, APIs, microservices, and CI/CD processes. Provide production troubleshooting, optimization, incident resolution, and ongoing enhancements across cloud environments. Translate client requirements into practical AI solutions, evaluate emerging AI technologies, and explain complex concepts to technical and non-technical stakeholders. Korean bilingual proficiency is mandatory.

Lead the architecture and production delivery of agentic AI solutions across insurance software products and workflows. Responsibilities include building reusable agent services, writing production code, designing orchestration and RAG workflows, integrating enterprise knowledge and APIs, establishing AI evaluation, implementing CI/CD and observability, enforcing security and governance, and guiding engineering teams on AI-enabled development practices.

Build and own Dotmatics Luma’s production agentic AI system, including reasoning loops, tools, memory, state persistence, security, sandboxing, recovery, evaluation, and observability. Develop supporting services and interfaces in Python, Node.js/TypeScript, and React. Lead AI-native development using coding agents, improve reliability and performance, and coach others on agent-driven engineering practices.

Design, develop, and operationalize AI/ML capabilities within secure DoD environments. Build agentic workflows, APIs, production services, and MLOps processes for classification, summarization, enrichment, and intelligent routing of lessons-learned data. Manage model deployment, validation, monitoring, and continuous improvement using Databricks, MLflow, and related tools. Integrate structured and unstructured data, troubleshoot production systems, and collaborate with engineering and mission teams while applying responsible AI and security practices.

Secure AI/ML models, data pipelines, and AI-enabled applications by conducting vulnerability assessments, threat modeling, incident response, and security reviews. Build guardrails, monitoring, detection, access controls, encryption, and data-segmentation measures. Partner with engineering and analytics teams throughout the AI development lifecycle, assess third-party AI tools, document remediation plans, and translate emerging threats such as prompt injection, data poisoning, jailbreaks, and model-supply-chain risks into practical mitigations.

Leads AI governance and risk oversight by assessing AI systems, tools, applications, and third-party solutions. Evaluates testing, monitoring, controls, explainability, reliability, bias, drift, hallucinations, and human oversight. Develops governance policies, standards, approval processes, metrics, and executive reporting. Coordinates incident response, remediation, audit readiness, and cross-functional risk management across technology, cybersecurity, legal, privacy, compliance, data governance, and enterprise risk teams.

Architects and delivers enterprise AI, machine learning, generative AI, RAG, and agentic workflow solutions. Designs secure integrations across Claude, ServiceNow, cloud platforms, enterprise data, APIs, and business workflows. Leads architecture workshops, technical discovery, model development, evaluation, governance, observability, and responsible AI controls. Builds data pipelines, ML/NLP applications, dashboards, and production integrations while collaborating with clients, consultants, engineers, and stakeholders. Travel to clients and conferences may be required as needed.

Build and deploy enterprise AI solutions using Claude, LLMs, RAG, agentic workflows, tool use, and structured outputs. Integrate AI applications with ServiceNow and enterprise systems, develop secure APIs and supporting services, evaluate model quality, implement responsible AI safeguards, troubleshoot production workflows, and collaborate directly with customers. The role also contributes reusable accelerators, platform capabilities, documentation, and deployment best practices while supporting discovery, prototyping, implementation, and production rollouts.

Deploys enterprise AI and agentic workflows for customers using Claude, LLM integrations, RAG, tool use, structured outputs, and responsible AI controls. Builds full-stack integrations across ServiceNow and enterprise systems, develops prototypes and production solutions, evaluates AI quality and safety, troubleshoots deployments, and contributes reusable components to NewRocket’s AI platforms. The role is highly customer-facing and requires approximately 70% travel.

Deploy and operationalize enterprise AI workflows, agents, RAG systems, and integrations across ServiceNow and connected platforms. Build full-stack components, LLM applications, APIs, prototypes, and production solutions using Claude and other AI technologies. Collaborate with customers and internal teams on discovery, implementation, troubleshooting, evaluation, security, responsible AI, and continuous improvement. Develop reusable accelerators, platform capabilities, integration patterns, and deployment practices while traveling extensively to support customer engagements.

Architects and delivers secure enterprise AI solutions spanning machine learning, generative AI, RAG, agentic workflows, data engineering, cloud platforms, and ServiceNow integrations. Designs LLM applications using Claude, tool use, structured outputs, governance, evaluation, observability, and human-in-the-loop controls. Leads technical discovery, architecture workshops, prototypes, stakeholder collaboration, and production deployments while supporting predictive analytics, NLP, workflow automation, dashboards, and responsible AI practices.

Build and deploy secure, production-ready generative AI solutions for enterprise customers. Responsibilities include developing Claude-powered agents, RAG systems, workflow automations, APIs, integrations, and full-stack components; conducting evaluations; implementing responsible AI controls; troubleshooting deployments; and collaborating with customers and internal teams. The role also contributes reusable accelerators and platforms while requiring substantial customer engagement and travel.

Design, train, and deploy reinforcement and imitation learning policies for humanoid robots. Run physical robot experiments, improve sim-to-real transfer, maintain simulation environments and data pipelines, analyze deployment failures, and collaborate with hardware and firmware teams to improve policy robustness.

Leads AI and data engineering for private equity platforms, building production LLM applications, MCP services, agents, and governed data products. Manages contract engineers while remaining hands-on with coding, architecture, evaluation, security, data quality, CI/CD, and production reliability. Partners with investment, finance, compliance, and business teams to convert ambiguous workflows into reliable software and ensures accurate, auditable portfolio, valuation, fund performance, and investor reporting data.

Build and lead production-grade agentic AI systems for enterprise clients across AWS, OCI, and Azure. Own full-stack development, cloud deployment, CI/CD, LLM integrations, agent orchestration, memory and retrieval architectures, APIs, identity, security, and operational documentation. Engage directly with stakeholders, translate requirements into scalable systems, advise on model and cloud strategy, and help productize the company’s delivery approach.

Own the architecture and technical delivery of a shared, air-gapped AI platform across business units and deployment models. Lead cloud-native infrastructure, Kubernetes, MLOps, CI/CD, LLM inference, RAG, AI assurance, integrations, observability, security, modernization, and multi-tenant configurability. Translate customer and business needs into platform capabilities, govern architecture, manage cross-team delivery, and remain hands-on across the technology stack. The role requires semiconductor industry expertise and leadership of engineering organizations.

Develop and deploy production AI/ML and generative AI solutions for FDA modernization initiatives. Build RAG pipelines, LLM applications, machine learning models, APIs, data pipelines, and MLOps/LLMOps capabilities. Evaluate model quality, implement responsible AI safeguards, monitor production systems, and integrate AI services into enterprise applications. Collaborate with data, software, cloud, and product teams in an Agile environment while documenting solutions and mentoring less-experienced engineers.

Leads client-facing discovery and technical product development for AI initiatives. Designs, prototypes, deploys, and operates agentic systems, retrieval pipelines, enterprise integrations, and production-grade generative AI applications. Establishes evaluation, observability, safety, governance, and reliability frameworks while translating business needs into technical specifications. Requires strong Python and software engineering expertise, cloud and DevOps experience, retrieval architecture knowledge, and at least seven years of engineering experience, including production generative AI experience.

Leads complex global IT audits covering infrastructure, cloud, cybersecurity, AI risk, identity, data protection, resilience, and technology controls. The role directs cross-functional teams and third-party specialists, performs risk assessment and audit planning, advises on cyber resilience and governance, assures AI adoption and transformation programs, advances modern audit methodologies, and coaches IT audit professionals. It partners with senior technology and business leaders to strengthen technology risk assurance and remediation.

Lead client-facing AI discovery and technical product leadership while designing, prototyping, and productionizing agentic systems, retrieval applications, and enterprise AI workflows. Build full-stack software, evaluation and observability frameworks, safety guardrails, and cloud deployments. Translate business needs into technical specifications, prioritize use cases, and guide solutions from rapid proof of concept through secure, reliable production systems.

Supports cybersecurity reviews of AI prompts, agents, skills, workflows, and automation tools. Evaluates compliance with AI security, Responsible AI, and data protection requirements; documents findings and mitigation recommendations; updates testing criteria and documentation; and develops simple scripts and test cases. Requires familiarity with GenAI threats, cloud security, AI testing frameworks, and basic Python or JavaScript scripting.