Top AI Engineer Jobs

Supports the development, testing, and deployment of AI and machine learning pipelines in secure environments. Responsibilities include maintaining ML data ingestion workflows, writing Python and Bash code, containerizing applications with Docker, conducting unit tests and security scans, and participating in Agile ceremonies, code reviews, and backlog refinement.

Architects and leads enterprise-scale AI software systems, including novel AI frameworks, LLMs, MLOps, multi-cloud and air-gapped infrastructure. Serves as the technical authority for high-assurance machine learning, container security, DevSecOps, and enterprise architecture. Advises government and agency leadership, establishes security standards, leads vulnerability remediation, and aligns engineering roadmaps with mission objectives.

Rapidly prototype, evaluate, and productionize AI-native product features (LLM agents, natural-language interfaces, human-in-the-loop flows, RAG). Build secure, reliable data pipelines, observability, and scalable architectures; run experiments, measurement harnesses, and work directly with customers and cross-functional teams to ship high-impact features.

Designs, builds, and operates AI-powered solutions for go-to-market teams, including retrieval, context, evaluation, guardrails, agent workflows, and operational automation. Owns problems end to end from discovery through launch, measurement, and iteration while shaping data and system architecture. Establishes reusable AI tooling, improves engineering velocity through AI-augmented development, mentors engineers, and maintains high standards for reliability, privacy, security, observability, and customer trust.

Develop and deploy AI and machine learning solutions using LLMs, RAG, NLP, search, classification, personalization, and information retrieval. Build end-to-end model pipelines, operationalize orchestration, analyze large datasets, and monitor deployed model performance. Collaborate with business, product, project management, and technology teams to define AI initiatives, roadmaps, and milestones while delivering scalable, maintainable, and well-tested software.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Lead architecture and development of an enterprise agentic AI and MLOps platform. Build scalable backend services, SDKs, autonomous and multi-agent systems, evaluation and observability pipelines, memory and state infrastructure, and RAG capabilities. Establish engineering standards, improve developer velocity, collaborate with product and engineering teams, and mentor junior engineers. The role requires deep distributed-systems, Kubernetes, cloud, LLM orchestration, vector database, and agentic framework expertise.

Leads Gen AI software engineering and enterprise architecture efforts, driving design and code reviews, architectural standards, design patterns, and long-term technology strategy. Guides large-scale development projects, evaluates technical risks, supports cloud and microservices solutions, and partners with business and technology stakeholders. Mentors engineers, promotes CI/CD and quality improvements, resolves delivery blockers, communicates metrics, and ensures compliance with applicable policies and regulations.

Build and ship enterprise AI software using RAG, LLMs, autonomous agents, and full-stack web technologies. Develop AI orchestration, APIs, microservices, gateways, and user interfaces while supporting MLOps and LLMOps. Evaluate emerging AI techniques, implement testing and observability, optimize performance, and follow security, ethical AI, and regulatory requirements. Collaborate with researchers, product, design, and engineering teams, contribute to architecture decisions, mentor engineers, and scale AI capabilities into production.

Partners with business and technology teams to identify high-value AI opportunities, design and build AI-enabled workflow solutions, integrate LLMs and agent tooling with existing systems, and deploy them securely to production. Measures business outcomes, applies responsible-AI controls, documents solutions for ongoing ownership, develops reusable patterns, and coaches colleagues on practical AI adoption in regulated financial services environments.

This role involves designing and building next-gen payments platforms using blockchain and AI technologies, focusing on payment modernization, security, and regulatory compliance.

Design and develop a Brandeis-compliant, instructor-facilitated hybrid microcredential course on AI literacy and practical applications for Jewish professionals. Responsibilities include creating the syllabus, multimedia materials, learning objects, and aligned assessments; serving as the subject matter expert; translating complex AI and policy concepts into accessible learning experiences; and collaborating with instructional designers while meeting LMS requirements, review cycles, and project deadlines.

Build frontend experiences for autonomous AI agents, including conversational interfaces, memory and context views, live run timelines, approval flows, worker configuration, and reusable agentic components. Develop real-time interfaces using streaming, WebSockets, and server-sent events while managing complex client state, reconnection, partial results, and failure recovery. Collaborate with backend and agent engineers to shape APIs and deliver fast, accessible, polished products.

Build and maintain responsive web and mobile interfaces with React.js or React Native while integrating AI and LLM capabilities. Develop prompts, AI agents, copilots, chatbots, RAG workflows, and intelligent assistants using Python and agent frameworks. Collaborate with product, UX, and backend teams, contribute to architecture and code reviews, optimize performance, and evaluate emerging UI and AI technologies.

Build production-ready AI-native applications, agentic workflows, cloud infrastructure, data platforms, integrations, and DevOps systems supporting nuclear engineering workflows. Responsibilities may span front-end/full-stack development, agentic AI, data and systems integration, or platform infrastructure. The role partners with systems engineering, licensing, and quality teams, documents architectures, maintains development and QA standards, and supports reliable, compliant delivery in a hybrid environment.

Develop production-ready AI-native applications, agentic workflows, and cloud infrastructure supporting nuclear engineering workflows. Responsibilities may span React full-stack development, AWS Bedrock agent architectures, RAG and Graph-RAG, engineering data lakehouses and integrations, or platform infrastructure and DevOps. The role includes collaborating with systems engineering, licensing, and quality teams; documenting architecture; implementing testing, security, governance, and CI/CD practices; and supporting regulated engineering applications under general supervision.

The AI Developer will develop, test, and deliver NICE CXone AI solutions, focusing on chat bots and other AI systems, while also supporting production and development teams.

Design, build, and optimize software applications and systems; implement software architecture and design patterns; improve performance, scalability, and reliability; collaborate with cross-functional teams; conduct code reviews and ensure adherence to best practices.

Develop and maintain full-stack web applications using C#/.NET, ASP.NET, SQL Server, JavaScript, React or Angular, Azure, and microservices. Responsibilities include architecture, requirements analysis, coding, testing, debugging, deployment, production support, technical documentation, and troubleshooting across on-premises and cloud environments. The role also applies LLM coding agents, agentic coding, and prompt engineering while collaborating with business users, analysts, QA teams, and vendors.

Provides technical pre-sales and consulting support for Microsoft’s GitHub, Visual Studio, Azure AI, and cloud developer platforms. Leads demos, proofs of concept, hackathons, code-with sessions, and architecture workshops for enterprise development teams. Advises customers on secure, scalable cloud-native architectures, DevOps, CI/CD, and AI-assisted software development; resolves technical objections; collaborates with engineering; and promotes Microsoft solutions through customer and developer communities.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core. Responsibilities include designing vector and matrix-processing engines, optimizing power, performance, area, and timing, developing detailed specifications, supporting functional verification, debugging simulations, and partnering on physical design validation. The role requires expertise in high-performance processor architecture, datapath design, vector and matrix processing, and hardware design languages.

Build and evolve ngrok’s AI Gateway, including real-time AI traffic handling, policy enforcement, rate limits, usage caps, access rules, token tracking, observability, and explainability. Design customer-focused gateway abstractions and collaborate with Gateway, Customer Data, Platform, product, and design teams to align enforcement, usage, and billing signals. The role involves full-stack development using primarily Go and TypeScript in a production AWS, Kubernetes, and distributed-systems environment.

The AI Compiler Engineer will innovate in AI performance, optimize compiler processes, and collaborate on hardware/software co-design, advancing AI technologies for datacenter applications.

Leads technical developer relations and strategic software-account engagement for NVIDIA integrations across observability, security, and AI platforms. Builds demos, sample code, guides, APIs, SDK integrations, and technical enablement programs; advises developers and partners; drives adoption and co-innovation; delivers talks and workshops; coordinates with engineering, product, sales, legal, and marketing; and translates ecosystem trends and developer feedback into product and platform strategy.

Build integrations that discover, identify, and govern AI agents across third-party platforms such as Salesforce Agentforce, Microsoft, AWS, and Google Cloud. Retrieve native identity signals, integrate them with Delinea’s identity layer, and govern agents through provider APIs and attestation. Improve shared discovery capabilities while using connector patterns, OAuth, token exchange, and event-based ingestion. The role requires substantial backend engineering experience, strong integration skills, and familiarity with cloud, AI agent, security, or identity ecosystems.