Top AI Engineer Jobs

Design, build, and ship production AI agents and ML systems for retail merchandising, document understanding, retrieval/ranking, and evaluation. Own full model lifecycle from research to production, build evaluation/observability, and collaborate with engineers, PMs, and store owners to drive measurable merchant impact.

Lead design and implementation of company-wide security architecture, perform penetration testing and vulnerability management, automate security tooling and CI/CD scanning, build observability and incident response systems, and collaborate across teams to scale security for AI compute and research infrastructure.

Lead technical architecture and delivery of end-to-end AI solutions across the Workday ecosystem. Design data flows, integrations, security and RAG/LLM patterns; advise executive stakeholders; lead technical workstreams; collaborate with Product and Engineering; ensure privacy, ethical AI, and scalable deployments for complex global customers.

Develop and maintain secure, cloud-native AI features and integrations for ontology-based applications. Migrate cloud platforms, integrate multiple data streams, implement backlog requirements, apply DevSecOps practices, and work on an agile scrum team to deliver scalable, tested software using containerization, microservices, CI/CD, and LLM/RAG-enabled capabilities.

Lead design and implementation of scalable DevOps platforms, architect CI/CD pipelines, manage IaC, optimize platform performance and security, automate deployments, collaborate with cross-functional teams, and mentor engineers.

Lead developer advocacy for AI and agentic security: advise security companies, accelerate NVIDIA stack adoption, guide partner integrations, map ecosystems, collaborate cross-functionally, run community events, and feed product teams with field insights.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Lead and define organization-wide Responsible AI, evaluation, and model-governance standards. Orchestrate cross-organization AI initiatives, influence engineering and executive stakeholders on architecture and roadmap, oversee end-to-end delivery, and mentor other program managers to drive strategic AI program outcomes and safety requirements.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Design and build the agentic platform for legal AI, including orchestration, retrieval-augmented generation, evaluation infrastructure, observability, and scalable backend services. Own systems from design through production, collaborate with product, research, security, and legal experts, and establish model-operations and multi-tenant correctness, traceability, and isolation.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

Design, develop, and deploy autonomous AI agents and ML models to automate cybersecurity use cases (incident investigation, vulnerability prioritization). Integrate LLMs, build data pipelines with vector databases, implement MLOps and cloud AI deployments, enforce safety/compliance guardrails, and optimize workflows for automated remediation.

Lead the security architecture and implementation for an enterprise AI platform: secure cloud infrastructure, data protection, IAM, tenant isolation, and GenAI workflows. Embed security into CI/CD, IaC, containers, secrets management, and operations. Design secure customer deployment patterns, support enterprise security reviews, and partner on compliance (SOC 2, ISO 27001, SSPA) while personally implementing critical security capabilities and guiding cross-functional engineering teams.

Design, build, and maintain scalable enterprise full-stack applications using Java, Spring Boot, microservices, and modern front-end frameworks. Implement cloud-based solutions on AWS, contribute to CI/CD and DevOps automation, optimize performance and security, and integrate AI-driven capabilities using Python and orchestration tools like LangChain and LangGraph.

Lead design and implementation of security controls for AI and cloud platforms, configure DLP and alerting, detect and remediate misconfigurations, perform AI security assessments, document findings, and advise clients and presales on AI security best practices.

Build and deploy machine-learning prototypes and tools for AI security; analyze large cybersecurity datasets; support AI red teaming and adversarial ML; collaborate with researchers to translate research into operational capabilities.

Design and implement ML-based prototypes, tools, and systems for AI security. Collaborate with researchers to execute experiments, analyze large cybersecurity datasets, support adversarial ML and red teaming, and translate research into deployable, maintainable software solutions.

Develop and deploy ML-based prototypes and tools for AI security, analyze large cybersecurity datasets, support adversarial ML and AI red teaming, collaborate with researchers and stakeholders, and translate research into operational capabilities while applying software engineering best practices.

You will build agentic AI products across web, backend, and iOS, owning features end-to-end and mentoring junior teammates.

Define, document, and validate low-level technical requirements for AI-enabled enterprise software. Work with customers, systems engineers, and developers to translate requirements into Jira stories, participate in Agile delivery, perform functional testing, and recommend AI approaches for integration and deployment on cloud-based microservices (AWS).

Design and ship features enforcing enterprise security policies for MCP access and usage. Improve deterministic and LLM-based risk policies. Deepen integrations with identity providers and strengthen the platforms security posture. Collaborate across product and engineering, participate in on-call rotation, and own features from design through production and support.

Design, build, and ship production AI systems and platform infrastructure across distributed compute, data pipelines, backends, frontends, and observability. Develop and improve multi-agent systems, inference infrastructure, benchmarks, verification pipelines, and feedback loops to improve agent quality, cost, latency, and robustness for drug-discovery workflows.

Designs, deploys, and optimizes AI-driven security workflows and agents (using Microsoft Copilot tools) to automate threat detection, incident response, and alert enrichment. Develops prompts and automations for threat hunting, triage, compliance reporting, creates dashboards (Splunk, Sentinel), maintains runbooks/SOPs, participates in on-call rotations, and collaborates with SOC, engineering, and GRC teams.

Build, integrate, test, secure, monitor, and operate agentic AI applications and platforms. Productionize AI proofs-of-concept into scalable, governed solutions; implement identity/security, sandboxing, telemetry, automated evaluation, tool orchestration, integrations, and reusable platform components; collaborate with architecture, security, governance, and application teams.

Design, build, and scale Python backend services and agentic LLM workflows for a multi-tenant, regulated enterprise platform. Productionize LLM integrations (Bedrock), ensure security/compliance, implement async orchestration, optimize data layers (Postgres, vector search/RAG), and add observability, real-time eventing, and typed API contracts. Collaborate cross-functionally and drive architecture to production.