Top AI Engineer Jobs

Develop web-based interfaces and software for embedded products, integrating REST APIs, WebSockets, networking, and AI agents. Collaborate with firmware, hardware, UX, testing, and product teams to deliver integrated solutions. Troubleshoot issues across interfaces, backend services, network communication, and embedded-device layers while contributing to design, testing, code reviews, documentation, and workflow automation.

Lead enterprise engineering strategy and delivery across full-stack systems, microservices, APIs, event-driven architectures, and applied AI solutions. Design and integrate reliable AI workflows, agent systems, retrieval, vector stores, and orchestration tools. Evaluate AI use cases, establish production monitoring and governance, mentor engineers, influence technical and product direction, and deliver secure, observable, high-performance services using modern cloud, container, database, and messaging technologies.

Build scalable, high-quality full-stack applications using Python, React, and AWS cloud services. Use AI-assisted agentic coding tools such as Claude Code or Cursor to accelerate development and improve engineering productivity. The role involves modern frontend development, cloud-based application development, scalable cloud-native architectures, and potentially integrating AI-powered tools and workflows into software development.

Leads enterprise AI Security and GenAI data protection, owning the AI Gateway from evaluation through deployment and ongoing operations. Designs controls for prompts, uploads, browser traffic, copilots, agents, and integrations; implements monitoring, coaching, redaction, quarantine, and blocking. Integrates telemetry with security and identity platforms, establishes governance and exception processes, measures effectiveness, supports audit readiness, and partners with business and technology teams to enable responsible AI adoption.

Build and deploy AI-powered workflows, agents, automations, and internal tools across business teams. Partner with teams to redesign processes, integrate AI with enterprise systems, establish governance, access controls, evaluations, monitoring, and cost tracking. Evaluate AI tooling, create reusable enablement patterns, train employees, document solutions, and promote self-service agent development while ensuring secure, permission-aware operation.

Develop, train, test, and evaluate AI/ML models and supporting software for aerospace and defense applications. Research and implement technical approaches, design reproducible experiments, analyze performance, investigate failures, and translate mission objectives into AI/ML tasks. Build simulation environments, data-processing tools, and evaluation workflows; contribute to multi-agent autonomy projects; integrate components into software frameworks; and communicate technical findings to teammates, leads, and customers.

Build reinforcement learning environments, evaluation frameworks, agent harnesses, context layers, backend systems, and data pipelines for creative AI applications. Design grading rubrics for subjective domains, collaborate with research teams and frontier AI labs, and ship production infrastructure end to end. The role is fully onsite in San Francisco five days per week.

Build and ship production AI-enabled product capabilities for transportation agencies. Partner with product, design, customer success, and data science to identify valuable AI applications, develop full-stack features, and integrate models into existing systems. Design LLM evaluations, ensure correctness, grounding, authorization, traceability, customer isolation, and prompt-injection protection. Own production reliability, latency, cost, monitoring, incident response, and continuous improvement across AI features.

Analyze contact center, customer, operational, and Voice of Customer data to identify trends, performance gaps, and process improvements. Build dashboards, analytics workflows, AI solutions, automation tools, and prototypes using Python and related technologies. Partner with business and technical stakeholders, analyze call and CRM data, develop coaching recommendations, and present actionable insights and business cases that improve agent productivity, call quality, customer experience, and contact center operations.

Build and deploy AI applications, agentic workflows, RAG systems, and automation tools for construction operations. Embed with project teams and visit jobsites to identify operational needs, prototype solutions, integrate construction data sources, and scale successful tools through Google Cloud Platform. The role requires strong coding, cloud architecture, AI engineering, and communication skills, along with collaboration across technical and field teams.

Intern will design and implement AI features for Zebra’s Form Creator and task-management products. Responsibilities include building Java/Spring REST APIs and Angular interfaces, adding responsible-AI guardrails, evaluating model quality, converging shared product capabilities, prototyping new AI-enabled products, writing tests, using CI/CD pipelines, documenting work, and presenting a final demonstration. The role is hybrid in Kennesaw, Georgia, with three days per week in the office.

Build production-grade AI applications using computer vision, multimodal AI, geospatial data, drone imagery, 3D environments, and sensor data. Develop Python services, APIs, data pipelines, and optimized models with PyTorch and ONNX. Support drone inspection, mapping, perception, and autonomy workflows while contributing to architecture, testing, CI/CD, deployment, and reliability. Collaborate with engineers and domain experts to prototype, evaluate, and productionize emerging AI techniques.

Design and build AI-powered cybersecurity platforms that ingest and normalize massive security telemetry, correlate events, predict threats, and generate explainable findings. Develop Python backend services, distributed storage systems, and AIDevOps pipelines for rapid experimentation. Integrate foundation models, agentic systems, RAG, embeddings, and graph-based models into analyst workflows. Collaborate with product teams, security experts, and engineers to deliver scalable, trusted threat detection and automation capabilities.

Build and productize AI-powered prototypes and tools for company-wide use. Responsibilities include evaluating AI solutions, developing LLM applications and MCP servers, collaborating with engineering and product teams, assessing complexity and cost trade-offs, working with vendors, presenting prototypes, and applying security analysis to AI systems.

Build, deploy, and support production AI tools, agents, integrations, and workflows. Develop LLM pipelines involving retrieval, extraction, scoring, prompting, and validation; integrate AI with Salesforce and Microsoft 365; monitor systems, investigate incidents, support users, and document solutions. Collaborate with stakeholders to turn ambiguous business needs into secure, scalable tools while applying least-privilege access, audit logging, and human-review safeguards.

Provides operational and engineering support for enterprise AI capabilities in secure Azure environments. Responsibilities include configuring AI services, managing access, developing CI/CD pipelines and infrastructure as code, implementing monitoring and alerting, troubleshooting production issues, coordinating root cause analysis, supporting security and compliance activities, and maintaining high availability and disaster recovery practices. The role also evaluates emerging AI and DevSecOps technologies, collaborates across technical teams, and maintains operational and governance documentation.

Build cloud-native, full-stack applications using Java, React, and Node.js while integrating AI services, intelligent agents, and automation workflows. Responsibilities include prototyping, testing, debugging, performance optimization, and production delivery. The role supports AWS or Azure development and uses AI-assisted coding tools such as GitHub Copilot, Claude, and Codex. Engineers collaborate with teams to deliver innovative AI-native applications and stay current with emerging technologies.

Designs, develops, deploys, and maintains scalable AI and machine learning solutions, including generative AI, agentic AI, deep learning, predictive models, data pipelines, and training workflows. Integrates AI systems into applications and business processes, especially in Azure environments. Collaborates with IT, data, and business teams; monitors model performance; ensures governance, cybersecurity, and responsible AI compliance; troubleshoots systems; and documents solutions.

Design and operate production infrastructure for AI agent systems, including high-performance Rust services for model inference, orchestration, and execution. Build scalable distributed systems, MLOps capabilities, observability, guardrails, and failure recovery for millions of users. Optimize latency, throughput, reliability, and cost while partnering with agent systems and infrastructure teams to harden experimental prototypes for production.

Build integrations that enable AI agents to interact with first-party and third-party applications. Develop structured agent actions, information retrieval interfaces, cross-application workflows, reusable SDKs, and reliable asynchronous APIs. Manage authentication, authorization, permissions, and integration limitations while collaborating with Android engineers on system-level application access.

Develop and modify Android system services, privileged APIs, and OS-level AI integrations. Build interfaces for AI agents to interact with Android functionality while managing permissions, IPC, application lifecycles, background execution, performance, and resource consumption. Collaborate with agent engineers and debug Android Framework or AOSP components. Preferred experience includes custom ROMs, AOSP contributions, C++, System UI, background services, and power management.

Build agent workflows, orchestration, tool integrations, context retrieval, memory, multi-step execution, and long-running stateful processes. Improve reliability through evaluations and regression testing, integrate local and cloud model providers, and support model routing and inference optimization. The role requires backend or AI application engineering expertise, strong API and distributed systems knowledge, and proficiency in Python, Go, or TypeScript.

Integrate AOSP or custom Android ROMs with smartphone hardware, including BSPs, vendor HALs, firmware, drivers, sensors, cameras, audio, modems, Wi-Fi, and Bluetooth. Support device bring-up and boot processes, troubleshoot hardware and firmware compatibility, collaborate with ODMs, OEMs, and chipset vendors, and optimize power consumption and hardware performance.

Evaluate, deploy, and optimize machine learning models for on-device execution on Android phones. Benchmark models on mobile hardware, optimize latency, memory, and power consumption, and apply quantization, compression, and distillation. Integrate models with Android applications, system services, and AI agent runtimes. Build hybrid local/cloud model routing and determine which workflows should run locally versus through cloud inference.

Develop and modify Android Framework components, system services, privileged APIs, and secure AI-agent integrations. The role focuses on application lifecycle, permissions, background execution, first-party app integration, OS stability, performance, and collaboration with agent engineers. Candidates need strong Android development experience, Java or Kotlin proficiency, AOSP knowledge, and familiarity with Binder IPC, permissions, and complex Android debugging. Custom ROM, C++, Linux, Android security, power management, and system-level shipping experience are preferred.