Top AI Engineer Jobs

Embed with core operational teams to map workflows and remove manual work by building production AI agents, copilots, and internal tooling. Deploy retrieval, memory, and evaluation systems to improve reliability, automate underwriting and claims workflows, extract structure from messy healthcare documents, and measure operational leverage through faster workflows and reduced manual processes.

Own and architect the frontend foundation for Handshake AI Enterprise: establish component system, design tokens, engineering patterns, shared libraries, and ship production-grade UIs while collaborating with AI and platform teams to ensure scalable, maintainable, high-performance enterprise deployments.

Evaluate and optimize NVIDIA AI infrastructure by running multi-node training/inference on large GPU clusters, designing benchmarks, profiling deep-learning workloads, identifying bottlenecks, producing tutorials and whitepapers, analyzing competitors, and presenting demos at major conferences.

As an AI Research Engineer, you'll conduct research in AI and automated reasoning, creating innovative solutions, testing systems, and contributing to scientific publications while collaborating with engineering teams.

The AI Workflow Engineer will design and deliver training, facilitate workshops, redesign workflows, and develop use cases for enterprise clients using AI tools.

Design and operate large-scale GPU infrastructure for distributed AI training, ensuring reliability, performance, and efficient customer partnerships.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

The Site Reliability Engineer will provision and manage Kubernetes clusters, build automation tools, debug customer issues, and improve infrastructure reliability.

As a Founding Full-Stack Engineer, you'll build AI-native systems, integrating LLMs, prototyping quickly, and collaborating to influence engineering standards and culture.

Design and implement backend services for a scalable agentic AI platform: agent identity and authentication, credential management, knowledge/memory layers, observability, governance and safety, orchestration for multi-step agent workflows, and cross-team integration with Product, SRE, Security, and Data Platform.

Lead technical sales and implementations for Nitra's AI platform: drive technical discovery, design, demos, POCs, integrations, and go-live. Ensure customer adoption, collect feedback to influence product roadmap, build scalable implementation playbooks, and troubleshoot complex issues alongside Engineering.

Lead technical discovery, solution design, demos, proofs-of-concept, and implementations for Nitra's AI platform. Partner with Sales and Customer Success to win deals and drive adoption, while feeding customer insights into Product and Engineering. Build scalable implementation processes, documentation, and resolve complex technical issues to ensure successful customer launches and expansion.

Design and build AI-powered tools to accelerate incident detection, triage, root-cause analysis, and automation for Credit trading production. Partner with support, development, and AI teams to embed AI into workflows, maintain high availability, analyze logs/metrics, and measure reductions in MTTR and operational toil.

Design, build, and productionize Applied AI foundations: model access, retrieval/memory, observability, secure data paths, human-in-the-loop workflows, and reusable AI-driven platform capabilities that support product and operational teams.

Build and maintain end-to-end mobile trading systems for iOS and Android. Ship production-quality, well-tested code, apply blockchain and crypto knowledge, and integrate AI tools to improve engineering workflows. Break down projects, collaborate cross-functionally, conduct and act on code reviews, mentor teammates, and participate in hiring and onboarding. Influence product decisions through data-driven insights and provide domain expertise for mobile trading initiatives.

Build the AI runtime for trustworthy agents: design orchestration, entity resolution, context assembly, connector orchestration, evidence retrieval, permissions and action policies. Own end-to-end features, prototype rapidly, implement evaluation frameworks, and collaborate with Product, Design, and Customer teams to deliver production-grade agent capabilities.

Principal AI Forward-Deployed Engineer designs and builds production-grade AI and agentic systems, leads forward-deployed projects with business stakeholders, authors scalable cloud architectures (AWS), implements MLOps/DevOps best practices, and ensures operational excellence, security, and measurable business outcomes.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Lead design, build, and scaling of Agentic AI and industry-specific solution accelerators on Genesys Cloud. Develop reusable multi-agent workflows, RAG-based knowledge orchestration, integrations with enterprise systems, prototypes, and production-grade AI applications. Provide technical leadership, mentor engineers, collaborate with product and industry SMEs, and deliver repeatable solution blueprints to accelerate deployment and time-to-value.

Build and launch AI-powered user-facing features on a Ruby on Rails backend. Integrate LLMs (OpenAI, Google, Anthropic), own zero-to-one product initiatives, write well-tested maintainable code, and mentor engineers while collaborating with product, design, and cross-functional teams.

Lead design and delivery of AI-enabled engineering capabilities across the SDLC to improve developer productivity, code and test generation, CI/CD integration, and secure, governed automation. Define portfolio-level technical strategy, implement reusable automation patterns, and partner with architects, security, and platform teams to scale AI-assisted workflows from pilot to enterprise adoption.

Senior individual contributor leading AI adoption in firmware engineering: evaluate AI tech, prototype and scale LLM/agent systems, build AI-assisted CI/CD and IDE tooling, architect secure AI infrastructure, and drive upskilling and cross-functional partnerships to improve firmware quality and velocity.

Lead design and ownership of an Azure-based production AI platform: integrate and standardize LLM tooling (orchestration, observability, prompt/versioning), build secure backend and CI/CD IaC pipelines, harden production operations, enable auditable regulated workflows, and mentor engineers to ensure reliable, traceable AI-assisted certification workflows.

Lead architecture and implementation of production agentic AI systems: build agents that plan, retrieve context, call tools, and execute workflows; design architectures for memory, tool routing, state, safety, and human approvals; create evals and observability for multi-step tasks; integrate with APIs and enterprise systems; improve reliability and guardrails; choose models and infrastructure; mentor senior engineers and own technical direction.

Design, deploy, and operate production-grade Generative AI agents and GenAI workflows for clinical trial optimization. Build cloud-native infrastructure, CI/CD, monitoring, evaluation, and scaling using AWS, containerization, CDK, and LLM frameworks. Collaborate with AI engineers and data scientists to transition research to reliable, explainable, and cost-efficient production systems.