Top AI Engineer Jobs

Lead secure deployment and operation of AI and ML systems, including AI gateways, policy orchestration, security automation, and infrastructure-as-code controls. Establish an AI red-teaming program addressing prompt injection, jailbreaks, model extraction, poisoning, data leakage, and supply-chain risks. Own the AI security roadmap, advise senior leadership, apply the OWASP LLM Top 10, partner with platform teams, mentor engineers, and provide vulnerability remediation guidance.

Lead the architecture and development of production AI/ML systems, including LLM applications, RAG, tool use, multi-agent workflows, and real-time inference. Partner with product, design, data, and leadership to ship scalable capabilities. Implement AI security, safety, privacy, reliability, observability, and human-in-the-loop controls. Mentor engineers and help shape technical practices in a fast-moving fintech startup.

Leads the architecture, development, deployment, and continuous improvement of enterprise AI solutions, including RAG pipelines, agentic workflows, LLM integrations, and AI microservices. Establishes LLMOps, model evaluation, security, privacy, and performance standards across Azure and Google Cloud. Develops data preparation and embedding pipelines, evaluates foundation models, conducts technical reviews, and mentors team members. Requires onsite work four days weekly and 10–15% travel.

Build and operate a high-performance AI Gateway that securely routes, inspects, and governs enterprise traffic to LLM providers. Develop asynchronous Python and FastAPI services, streaming APIs, network protocols, cloud-native infrastructure, messaging systems, security integrations, observability, and CI/CD pipelines. The role involves Kubernetes deployments, scalable concurrent connections, identity and access management, AI/LLM workflows, cost optimization, and incident response. Korean-English bilingual ability is highly preferred.

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

Owns quality engineering for enterprise generative AI products, including evaluation harnesses, regression suites, quality gates, observability, responsible AI validation, and release readiness. Develops Python automation for APIs, data pipelines, RAG systems, and conversational workflows; measures accuracy, groundedness, safety, latency, cost, and reliability. Partners with engineering, product, UX, and domain experts, leads root-cause analysis, communicates risk, and mentors teams on testing non-deterministic AI systems.

Design and build reusable enterprise platforms for production-grade AI agents and intelligent applications. Develop agent orchestration, RAG and knowledge retrieval, tool integrations, evaluation, observability, lifecycle management, APIs, SDKs, and cloud-native services. Implement CI/CD, testing, security, guardrails, reliability, monitoring, and cost optimization for AI workflows. Collaborate with engineering, data, product, architecture, and domain teams to operationalize emerging AI capabilities at enterprise scale.

Build and operate a shared AI platform for internal application development, governed data access, knowledge retrieval, MCP infrastructure, agent orchestration, model evaluation, and cost optimization. Establish AWS infrastructure, security controls, observability, governance, and automated workflows supporting R&D, manufacturing, and engineering teams. Lead technical designs, mentor engineers, and collaborate with scientists and operators in a hybrid onsite role.

Owns company-wide AI security by assessing tools, models, agents, and integrations; identifying prompt injection, data leakage, and misuse risks; implementing access, DLP, logging, and monitoring controls; partnering with Engineering on secure AI design; maintaining threat models, standards, risk tiers, inventories, and incident response playbooks; and advising teams on safe AI adoption.

Secures CI/CD pipelines, cloud infrastructure, software supply chains, and MLOps workflows. Builds controls for AI and LLM security, threat models AI systems, and develops internal AI agents for security automation. Partners with engineering teams on secure development practices, manages secrets and Kubernetes hardening, supports incident response, and reports on DevSecOps maturity and AI security posture.

Build reliable autonomous AI agents for identity operations and security. Responsibilities include developing agent harnesses, integrating OpenAI and Anthropic models, creating evaluation datasets and replay tests, measuring model quality and safety, protecting tenant boundaries, and improving prompts, retrieval, tools, and model routing. The role requires strong Python engineering, production LLM application experience, agent workflow or evaluation expertise, and ownership across development, release, and customer use.

Build reliable autonomous AI agents for identity operations and security. Develop agent harnesses for planning, tool use, memory, recovery, authorization, and workflow execution. Integrate and evaluate OpenAI and Anthropic models using quality, latency, cost, and safety metrics. Create evaluation datasets, replay tests, regression tests, and protections against prompt injection and unsafe actions. Own production improvements across code, evaluation, release, and customer use.

Secure the design, development, deployment, and operation of enterprise AI solutions. Evaluate AI platforms, machine learning models, and AI-enabled applications for cybersecurity, privacy, compliance, and third-party risks. Conduct threat modeling, architecture reviews, and security assessments; recommend controls for data protection, model security, identity, access, governance, and supply-chain security. Contribute to AI security strategy, governance, standards, and roadmaps while educating stakeholders and promoting secure-by-design practices.

Build reliable autonomous AI agents for identity operations and security. Responsibilities include developing agent harnesses for planning, tool use, memory, recovery, model routing, authorization, evaluation, and long-running workflows. Integrate OpenAI and Anthropic APIs, measure quality and safety, create replay tests, investigate failures, protect tenant boundaries, and improve prompts, retrieval, tools, and models. The Principal Engineer will set product engineering direction and develop shared components for production AI systems.

Serves as PDW’s senior AI/ML technical leader, defining enterprise AI strategy, architecture, standards, and roadmap while building production systems. Develops RAG solutions, AI agents, orchestration platforms, evaluation frameworks, APIs, data pipelines, and MLOps infrastructure. Leads AI security and governance, optimizes models across cloud and GPU environments, partners across engineering and business teams, and mentors teams adopting AI technologies. Requires extensive hands-on experience with generative AI, production ML systems, secure architecture, and enterprise deployment.

Develop and maintain AI platform infrastructure, APIs, microservices, and data-processing systems supporting model and agent deployment. Collaborate with AI/ML teams and cross-functional partners to deliver scalable, reliable solutions. Troubleshoot performance and integration issues, contribute to architecture, and support production systems in a continuous-deployment DevOps environment.

Build and scale customer-facing Android marketplace applications using Kotlin, Compose, and modern Android architecture. Own complex features end to end, lead technical direction, improve testing and CI/CD, support releases and observability, and modernize legacy codebases. Use AI tools for coding, testing, debugging, documentation, and operational efficiency while mentoring engineers and collaborating with product, design, backend, platform, QA, and iOS teams.

Build and operate a secure, locally hosted LLM platform for enterprise engineering and business use. Responsibilities include model evaluation and optimization, inference serving, RAG and search pipelines, permission-aware retrieval, security controls, evaluation frameworks, observability, safe agent tooling, integrations, CI/CD, reliability, and lifecycle management. The role leads architecture and roadmap decisions while partnering cross-functionally to deliver measurable, production-ready generative AI capabilities.

Design and build Docker’s AI Gateway, including model and tool routing, MCP services, control-plane infrastructure, secure credential management, policy enforcement, observability, caching, rate limiting, and agent evaluation systems. Lead architectural decisions, optimize performance and reliability at scale, guide roadmap planning, and mentor engineers while creating developer-facing APIs for AI applications.

Lead architecture and development of Netskope’s secure AI Gateway, building scalable, high-performance systems that route, inspect, transform, and secure LLM and API traffic. Responsibilities include designing distributed microservices, threat detection, dynamic multi-provider orchestration, credential management, DLP integration, and real-time content analysis. The role provides technical leadership, mentors engineers, partners with product teams, conducts reviews, and guides global engineering collaboration.

Lead the architecture and implementation of Docker’s AI Gateway, including model and tool routing, MCP, control-plane systems, credential vaulting, policy enforcement, observability, analytics, caching, and rate limiting. Solve complex distributed-systems problems, establish technical direction, improve infrastructure, mentor engineers, and shape product strategy. Build secure, scalable services supporting model orchestration, agent evaluation, replay, and enterprise-grade governance.

Build, deploy, secure, and operate full-stack enterprise applications with AI-enabled capabilities. Partner with business stakeholders to translate needs into production software, including LLM applications, retrieval-augmented generation, AI agents, integrations, and automation. Own applications throughout their lifecycle, including architecture, testing, CI/CD, monitoring, incident response, vulnerability remediation, modernization, and retirement. Collaborate with security, architecture, data, infrastructure, and product teams while establishing responsible AI controls and mentoring engineers.

Designs, builds, deploys, secures, and operates full-stack enterprise applications with AI-enabled capabilities. Partners directly with stakeholders to translate business problems into production software, prototypes, APIs, integrations, and intuitive user experiences. Develops LLM, RAG, agentic, and automation solutions with testing, evaluation, guardrails, observability, and security controls. Owns application lifecycles, including monitoring, incident response, vulnerability remediation, modernization, documentation, and continuous improvement. Requires strong full-stack, cloud, DevOps, application security, and AI engineering experience.

Build and lead MHA’s agentic AI engineering capability across Azure and M365. Develop agent orchestration patterns, MCP integrations, evaluation frameworks, security guardrails, CI/CD pipelines, LLMOps observability, and cost governance. Establish responsible AI practices for regulated data, including PHI, while partnering with engineering, security, and operations teams. Create standards, tooling, training, and prototypes that accelerate software delivery and increase organizational adoption of agentic development.

Build and own Supabase’s internal AI execution platform, including event-driven agent runtimes, durable state, human review, rollback, observability, evaluations, governance, and least-privilege tooling. Develop production agents, MCP integrations, infrastructure on Python and GCP, CI evaluation gates, operating metrics, and return-on-investment instrumentation. As the sole platform engineer, independently make architecture decisions, operate systems end to end, and ensure unsafe actions are structurally impossible.