Top AI Engineer Jobs

Supports pre-sales execution for AI security solutions through technical discovery, architecture reviews, demonstrations, proof of concepts, deployment assistance, partner enablement, and customer training. The role develops sales engineers, manages demo environments, supports channel partners, facilitates technical handovers, and engages CTOs, CISOs, and AI leaders. It requires AI security expertise, cloud deployment experience, containerization, Kubernetes, and strong technical communication skills, with travel up to 50%.

Support pre-sales execution for AI security solutions by conducting technical discovery, architecture reviews, demonstrations, proofs of concept, deployments, and training. Enable sales engineers and channel partners, manage demo environments, support customer handoffs, and build relationships with technical decision-makers. Promote secure AI and agentic AI deployment practices while helping identify, qualify, and close opportunities across existing and prospective customers.

Lead technical pre-sales for Fortinet’s AI security solutions, supporting discovery, architecture reviews, demonstrations, proofs of concept, deployments, partner enablement, and customer training. The role requires deep knowledge of AI technologies, cloud platforms, containers, Kubernetes, and AI security practices. It also supports sales strategy, technical handoffs, customer relationships, and evangelism while traveling up to 50%.

Designs, operates, and optimizes secure infrastructure for agentic AI and production machine-learning workloads. Leads GPU and accelerator platforms, Kubernetes-based delivery, model serving, distributed AI infrastructure, observability, FinOps, automation, and high-performance networking and storage. Partners across engineering, security, risk, and platform teams to ensure reliability, scalability, compliance, cost efficiency, and operational readiness while mentoring junior engineers.

Designs and operates scalable infrastructure for agentic AI, model training, inference, and distributed workloads. Responsibilities include Kubernetes and GPU platform engineering, infrastructure as code, high-performance storage and networking, observability, FinOps, security, capacity management, testing, documentation, and operational support. The role also integrates AI-assisted engineering and AIOps tools into deployment, monitoring, troubleshooting, and infrastructure workflows while partnering with AI, security, risk, and platform teams.

Design, develop, and maintain full-stack C# and .NET applications for distributed Azure cloud systems. Build business functionality, integrate generative AI and LLM capabilities, containerize applications, and support production reliability. The role also involves testing, code reviews, troubleshooting, documentation, and collaboration with cross-functional teams to deliver scalable, maintainable software.

Build and support reliable dashboards for AI infrastructure and financial models, including TCO, tokenomics, and compute capital models. Responsibilities include implementing features, loading model releases, validating data, developing accuracy checks, collaborating with analysts, creating charts and explanations, answering client questions, and contributing to research newsletters. The role requires strong Python or TypeScript skills, software engineering experience, numerical accuracy, and effective use of AI coding agents.

Develop and evaluate machine learning and LLM-based systems, including neural network tuning, model development, benchmarking, experiment design, and performance validation. Use Python or R, deep learning frameworks, SQL, GPUs, and version control systems. Preferred work includes Hugging Face, cloud architecture, semantic search, Tableau, Explainable AI, ONNX, and web development. Requires U.S. citizenship and an active TS/SCI with Polygraph clearance.

Leads the design, development, testing, deployment, and architecture of production-grade LLM and agentic applications. Builds retrieval, tool-calling, orchestration, evaluation, guardrail, monitoring, and observability systems. Establishes AI-assisted development standards, reviews AI-generated code, mentors engineers, diagnoses model and prompt issues, and collaborates cross-functionally on AI strategy and product delivery.

Develop and characterize ReRAM technology, including device behavior, variability, reliability, modeling, and array analysis. Build AI-enabled engineering solutions using machine learning, generative AI, physics-informed AI, and AI agents for semiconductor modeling, simulation, EDA automation, data analysis, and workflow improvement. Collaborate across semiconductor engineering teams and translate technical challenges into practical software, modeling, and automation tools.

Lead the design and engineering of an AI-native experience layer for service operations. Build real-time operational visualizations, agent-to-agent interfaces, APIs, and tools that make automated decisions visible and trustworthy. Own UI/UX, information architecture, prototyping, production delivery, technical direction, and mentoring. Partner with AIOps and operations teams to support incident response through interactive telemetry, topology, alert, timeline, and predictive-signal experiences.

Leads end-to-end data engineering solutions, including scalable Snowflake and dbt pipelines, data modeling, SQL transformations, CDC, ELT, quality frameworks, monitoring, and production deployment. Designs engineering standards, troubleshoots complex data issues, improves performance and cost efficiency, collaborates with stakeholders, leads technical reviews, and mentors engineers.

Administer enterprise AI platforms, including access controls, API keys, configurations, monitoring, and observability. Build and maintain Python or JavaScript/TypeScript integrations, scripts, automations, REST API connections, vector-store pipelines, and retrieval workflows. Troubleshoot production issues, manage deployments and documentation, monitor resource usage, and collaborate with DevOps, Data Engineering, and InfoSec teams on reliability, security, and compliance.

Build AI-powered development tools, intelligent agents, automated workflows, and full-stack platform services. Develop React, JavaScript, and TypeScript frontends alongside C# and .NET Core backend services and REST APIs. Create automated testing and validation workflows, integrate AI solutions into engineering processes, and improve productivity, reliability, and scalability. Collaborate with globally distributed teams while using tools such as Claude Code to streamline coding, debugging, documentation, and operational tasks.

Provides enterprise technical leadership for AI platform engineering and responsible AI adoption. Designs and operates platforms supporting LLMs, retrieval-augmented generation, vector search, agents, and AI-assisted development. Establishes governance, security, monitoring, evaluation, cost controls, and production reliability. Leads complex engineering initiatives, advises executives, connects technology strategy to business value, and mentors technical leaders across architecture, cloud, DevSecOps, testing, and observability.

Build and operate Kinetic’s AI agent platform, including orchestration, model routing, tools, memory, permissions, guardrails, CI/CD, evaluation infrastructure, observability, and human-in-the-loop workflows. Develop secure MCP integrations with enterprise and claims data, ensure privacy and auditability, establish rollout standards, replatform existing AI workflows, mentor engineers, and guide build-versus-buy decisions. This is a hands-on senior individual contributor role requiring production LLM expertise and collaboration with product and platform teams.

Deploys, tunes, and sustains Vectra AI sensors, collectors, and metadata pipelines across physical data centers, air-gapped networks, and on-premise environments. Integrates NDR telemetry with SIEM, SOAR, EDR, packet brokers, and analysis tools. Engineers sensor placement, routing, VLANs, packet capture, metadata transformation, secure configurations, monitoring dashboards, and health checks. Requires network engineering or cybersecurity platform experience, Linux expertise, strong packet and protocol knowledge, and an active TS/SCI clearance with polygraph eligibility.

Build and deploy AI tools, agentic systems, internal services, workflows, interfaces, and integrations across the company. Drive AI tooling adoption, improve software development workflows, create reusable connectors and guardrails, evaluate build-versus-buy options, and partner with infrastructure, security, operations, and business teams. Measure productivity, adoption, and user trust while ensuring safe, maintainable production systems.

Develop production-grade AI features while serving as the on-site technical lead and client liaison. Responsibilities include translating business requirements into software, leading product discovery and architecture reviews, communicating with executive stakeholders, coordinating distributed engineering teams, managing agile delivery, and implementing full-stack LLM solutions using APIs, prompt engineering, RAG, and tool calling.

Sets WGU’s enterprise AI operations and platform strategy, defining architectures, governance, reliability, security, observability, and responsible AI standards. Leads high-impact AI platform and migration programs, advises executives, evaluates emerging technologies, builds engineering culture and teams, mentors engineers, partners with legal and compliance, and represents the organization through conferences, publications, and strategic partnerships.

Build and productionize enterprise AI solutions from problem discovery through technical validation. Partner with business, architecture, data, security, risk, and compliance teams to develop AI applications, agents, copilots, and workflow solutions. Integrate AI with enterprise systems, APIs, data platforms, and knowledge repositories using RAG, agentic workflows, vector search, and evaluation techniques. Establish monitoring and reliability practices, create reusable components, and transition mature solutions to delivery teams.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Build and operate production-grade private AI platform capabilities, including agentic and multi-agent workflows, orchestration, evaluation, monitoring, fine-tuning, runtime optimization, safety controls, APIs, and rollback mechanisms. Partner with data and infrastructure teams to improve reliability, performance, and deployment quality. Senior engineers also mentor teammates, review designs, and lead major platform components.

Designs, develops, tests, and deploys AI-enabled applications, dashboards, workflow automations, reporting tools, and system integrations. Connects insurance platforms through APIs and middleware, supports data flow across rating, billing, claims, CRM, and analytics systems, and performs quality assurance. Translates operational requirements into scalable technical solutions while collaborating with underwriting, claims, finance, leadership, vendors, and other stakeholders. Requires full-stack or backend development experience and production experience with AI/ML or LLM applications.

Design and develop real-time voice-to-voice AI agents and conversational systems using LLMs, agent frameworks, speech recognition, speech synthesis, and streaming technologies. Build Python and C#/.NET backend services, APIs, integrations, memory, tool-calling, and RAG workflows. Optimize latency, monitor reliability, evaluate emerging AI technologies, and collaborate with product and engineering teams to deliver production-ready voice solutions.