Top AI Engineer Jobs

Leads the design, implementation, administration, and support of enterprise Kong AI Gateway solutions. Develops secure LLM proxies, integrates multiple AI providers, configures guardrails and governance policies, and implements routing, observability, token analytics, cost controls, and RAG integrations. Manages Kong in Kubernetes environments, automates deployments, monitors performance, and drives platform reliability, scalability, compliance, and enterprise GenAI adoption.

Design and implement AI/ML solutions for cyber and intelligence workflows, including LLM-powered workflows, agent-based automation, and API or enterprise data integrations. Partner with mission users, analysts, developers, and leadership to document operational processes, build reliable production capabilities, and meet classified-environment security and governance requirements.

Lead the development of large-scale video foundation models for humanoid autonomy, focusing on training strategies and model evaluation for real-world applications.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

Provides technical leadership for designing, building, and operating secure, reliable, scalable AI products. Leads generative AI, agentic, retrieval, and evaluation systems from concept through production; defines architecture and engineering standards; evaluates emerging models and frameworks; partners cross-functionally; mentors engineers; and resolves complex technical risks and trade-offs.

Develop and support secure AI-powered and web-based applications, APIs, integrations, and automation solutions. Build LLM applications using retrieval-augmented generation, vector search, embeddings, tool calling, and agentic workflows. Integrate enterprise systems and databases, apply secure software practices, support deployments, and partner with stakeholders to deliver maintainable production solutions.

Develop and deploy autonomous AI agent workflows, RAG systems, and LLM integrations for financial planning and analytics. Build forecasting models for revenue, EBITDA, and other metrics while ensuring workflows are auditable, explainable, scalable, and compliant with financial data governance standards. The role requires Python-based AI development, LLM orchestration, vector databases, prompt engineering, and translating financial business logic into production AI architectures.

Develop high-performance AI kernels and software for neuromorphic hardware. Optimize convolutional, transformer, and generative AI workloads using parallelism, sparsity, quantization, fusion, and related techniques. Build performance, power, and area models, reference implementations, benchmarks, validation tools, and regression infrastructure. Collaborate with hardware, compiler, runtime, and application engineers throughout the hardware-software lifecycle.

Build scalable infrastructure for LLM-based agentic AI supporting autonomous security operations. Responsibilities include designing agent orchestration frameworks, LLM inference and resource-management systems, MCP servers, supporting APIs, and distributed backend services. The role collaborates with machine learning, security research, engineering, and product teams. Required experience includes backend infrastructure, distributed systems, APIs, microservices, cloud deployment, containerization, orchestration, and Python, Java, or Go.

Leads the architecture, deployment, operation, and evolution of Verisign’s on-premises AI and LLM platform. Designs production inference systems, evaluates models and tools, develops evaluation and observability practices, enables secure AI-assisted software engineering, and establishes deployment, security, reliability, and governance standards. The role prototypes emerging technologies, converts successful experiments into production services, mentors engineers, and guides enterprise AI adoption.

Engineer and administer enterprise AI platforms, integrations, access controls, security guardrails, and governance. Build MCP servers, API wrappers, automation, and lightweight services; manage deployment through infrastructure as code and CI/CD. Evaluate new AI capabilities, troubleshoot integrations, advise business users, implement telemetry, and identify opportunities for responsible AI adoption while balancing innovation, cost, security, and enterprise risk.

Leads the engineering, integration, deployment, and support of AI-powered security operations platforms. Designs security automation, SOAR integrations, RAG architectures, agent orchestration, evidence pipelines, observability, governance, and cloud-based security workflows. Partners across security, risk, data, and technology teams to improve investigations and reduce response times while managing AI-related risks. Provides technical leadership, establishes engineering standards, mentors staff, and communicates complex security risks to senior stakeholders.

Conduct self-directed experiments to improve multimodal AI accuracy, including model fine-tuning, distillation, retrieval, context design, and computer vision evaluations. Build internal experimentation tools, review results, set research priorities, and engage regularly with users. The role combines rapid software engineering with rigorous machine learning experimentation and requires working onsite in San Francisco five days per week.

Designs and maintains secure, scalable cloud platforms using infrastructure as code, containers, CI/CD, automation, observability, and security practices. Partners with engineering teams to improve developer experience and platform reliability. Builds capabilities for AI-assisted and agentic development, including model access, MCP servers, tool integrations, identity management, governance, auditing, and observability. Researches emerging cloud and AI technologies and promotes responsible AI adoption.

Designs, tests, optimizes, and documents large language model prompts for scalable healthcare conversation scoring. Collaborates with engineering, product, implementation, and data-labeling teams to improve scoring accuracy, prompt workflows, tooling, and standardization. Maintains a reusable prompt library, evaluates prompts using labeled datasets, and identifies process inefficiencies. The role requires strong LLM, NLP, and prompt-engineering knowledge, with preferred experience in data labeling, LangChain, OpenAI or GPT APIs, vector databases, linguistics, HCI, and Python automation.

Deploys and supports enterprise AI assistants such as Microsoft Copilot and Claude, performs identity, security, data, licensing, and governance readiness activities, and manages AI platform services. Builds AI agents, automations, prototypes, and lightweight applications using APIs, low-code tools, and AI-assisted development platforms. Leads client workshops, training, adoption, troubleshooting, and responsible AI initiatives while translating business needs into secure, scalable enterprise solutions.

Build and optimize production AI pipelines for healthcare workflows using LLMs, classical ML, VLMs, OCR, retrieval, agents, and fine-tuned models. Develop document extraction systems, evaluation harnesses, monitoring, and inference orchestration. Improve model quality, latency, cost, explainability, and safety while collaborating with product, engineering, senior ML engineers, and healthcare domain experts.

Build and operate a secure, production-grade enterprise AI platform using AWS Bedrock, Claude, RAG, agents, orchestration, governed data connectivity, monitoring, security, and cost controls. Partner with business teams to identify, prototype, deploy, and measure AI use cases. Establish reusable patterns, enable AI champions, manage model evaluations and releases, and ensure reliable, compliant, permission-aware access to enterprise data.

Leads the design, deployment, and governance of AI-driven cybersecurity capabilities across enterprise environments. Develops AI-enabled threat detection, anomaly detection, and automated response solutions; configures and tunes security technologies; provides architecture guidance; analyzes AI-related threats and incidents; and partners with security operations, threat intelligence, engineering, governance, and endpoint teams. The role also drives secure AI configuration standards, enterprise platform integration, and technical leadership across cybersecurity initiatives.

Develop AI agents, chatbots, and machine learning models for a modernized federal data science platform. Research and standardize AI capabilities in Databricks and Palantir, prompt-engineer LLMs, integrate AI into DevOps pipelines, and train models on production datasets for prediction and anomaly detection. The role requires collaboration with developers and customers, database familiarity, Agile experience, documentation, and an active or obtainable DoD Secret clearance.

Leads hands-on design, development, testing, and production delivery of generative and agentic AI solutions. Builds RAG, automation, knowledge retrieval, developer productivity, and IT operations capabilities; integrates AI with enterprise systems, APIs, data, and cloud services. Ensures security, privacy, observability, evaluation, scalability, and operational readiness while mentoring engineers, documenting reusable patterns, and coordinating technical delivery across architecture, infrastructure, security, data, and business teams.

Design and build production AI applications, agentic workflows, RAG pipelines, and multi-step automations using LLM APIs and orchestration frameworks. Integrate tools, retrieval systems, prompts, APIs, and infrastructure; create evaluation and observability systems; monitor reliability, latency, cost, and output quality. Translate ambiguous business needs into practical technical solutions, collaborate with stakeholders, and identify opportunities for AI-enabled process improvement.

Supports the design, development, and governance of AI and ML architectures for enterprise modernization. Responsibilities include building cloud data pipelines, AI governance frameworks, agentic AI and RAG architectures, vector database integrations, model monitoring, explainability, bias detection, drift management, and architecture documentation. Collaborates with enterprise architects and engineering teams while supporting governance reviews and technical assessments.

Provide technical support to SaaS customers by debugging Playwright tests, API checks, authentication flows, and application issues. Use packet captures, traceroutes, and timing analysis to troubleshoot failures, create engineering reproductions, and contribute bug fixes. Improve AI-assisted support processes, identify product trends, maintain documentation, and collaborate asynchronously with product and engineering teams.

Lead design and delivery of agentic AI systems and LLMOps on AWS to generate, validate, and deploy infrastructure-as-code (Terraform). Build multi-agent applications, RAG pipelines, and AIOps for cloud operations; integrate AI into CI/CD and ITSM. Set technical direction, establish guardrails/policy-as-code, curate reusable Terraform modules, and mentor engineering teams.