Top AI Engineer Jobs

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Develop and test resilient, scalable multi-agent AI systems and RAG pipelines. Integrate agents with external tools and APIs, run LLM/RAG evaluations, write prompts, and support light model fine-tuning to improve network security product development efficiency.

Design, build, and ship production multi-agent LLM systems including orchestration, memory/retrieval, safe write-back to customer systems, evaluations and regression tests, adversarial hardening, LLM model-agnostic integration, and cost/latency/reliability ownership at enterprise scale.

Lead design, deployment, and management of AIOps solutions using Splunk and MLTK to provide predictive analytics, closed-loop incident remediation, and secure telemetry integration with ServiceNow and SolarWinds. Architect observability across classified/unclassified enclaves, deploy ML models, automate remediation, and ensure compliance with DoW STIGs and IL5/IL6.

The Senior Solutions Engineer will design and implement infrastructure for AI and HPC workloads, engage with customers, and lead technical discovery and architecture design.

Design, build, and maintain end-to-end agentic intelligence systems that ingest social, on-chain, and ads data; create pipelines, semantic layers, and multi-step AI agents; ship full-stack apps and dashboards; collaborate with marketing and client teams to operationalize structured intelligence and measurable impact.

Own and optimize on-board inference architecture for humanoid robots: map and partition models across heterogeneous accelerators, enforce compute budgets, optimize toolchains and kernels, apply quantization/pruning, profile pipelines, and collaborate with AI, platform, and silicon vendors to meet real-time power, latency, and reliability constraints.

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, 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.

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.

Design, build, and operationalize scalable AI/ML infrastructure and data pipelines. Implement model lifecycle management (MCP), integrate agent frameworks (e.g., LangChain), manage vector DBs and feature stores, enable HITL workflows, maintain data architectures, mentor junior engineers, and present work internally and externally.

Architects, develops, and deploys secure AI solutions for federal and defense organizations. Responsibilities include building RAG and LLM-agent systems, semantic search, NLP applications, predictive analytics, automated data pipelines, chatbots, explainable AI architectures, and interactive dashboards. The role integrates structured and unstructured enterprise data while ensuring compliance with DoD security requirements across classification levels.

Lead development of AI workforce platform infrastructure, including deployment automation, CI/CD pipelines, observability, configuration standards, prompt and skill marketplaces, MCP integrations, and workflow playbooks. Partner with engineering teams to embed AI tooling into development workflows, improve platform capabilities, document best practices, and provide technical leadership. The role includes production-quality coding, cross-functional communication, on-call participation, and business-hours customer support.

Conduct red-team assessments of AI-enabled systems, including models, hardware, software, and networks, supporting national security missions. Develop offensive tactics, techniques, procedures, and operational tools using Python, C, Bash, and PowerShell. Perform penetration testing, exploit development, reverse engineering, vulnerability analysis, and Windows/Linux security assessments. Communicate findings to mission partners, travel to field sites, and mentor colleagues.

Leads architecture and implementation of enterprise AI applications and agentic LLM workflows. Optimizes model inference, memory usage, quantization, and deployment for air-gapped environments. Designs multi-cloud Kubernetes infrastructure with Terraform, establishes DevSecOps security scanning and remediation, and drives Agile execution, technical debt management, and team mentorship.

Designs, builds, and integrates AI/ML solutions and microservices for defense and intelligence missions. Responsibilities include fine-tuning language models, developing RAG pipelines and vector search integrations, designing model training and data pipelines, deploying containerized services with Docker and Kubernetes, integrating security scanning into CI/CD, managing multi-cloud environments, and mentoring developers in Agile teams.

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.

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.

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.

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.

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.

Develop and deploy AI-enabled features, including LLM and RAG workflows, for ontology-based intelligence applications. Integrate data streams, migrate cloud platforms across AWS, OpenShift, and Kubernetes, and build secure, scalable software using Python, Java, and Scala. Participate in Agile/Scrum delivery, DevSecOps, testing, CI/CD, code hardening, and vulnerability remediation. Collaborate with infrastructure, security, and mission teams while supporting complex network deployments and mentoring junior developers.