Top AI Engineer Jobs

Leads enterprise AI strategy, architecture, and production implementation across corporate functions, contact centers, and GTM products. Designs generative and agentic AI solutions, conversational and voice agents, RAG systems, intelligent workflows, and multi-agent orchestration on Azure and Google Cloud. Establishes AI governance, observability, SecOps, escalation, and operational models while influencing executives, product teams, and engineering organizations. Mentors technical staff and drives measurable AI adoption and business outcomes.

Build and improve AI agents that automate engineering, fleet operations, and business workflows. Responsibilities include integrating LLMs with internal systems, implementing planning and tool-using agent workflows, developing backend services and APIs, evaluating models and frameworks, and contributing to internal AI platforms. The role requires strong programming fundamentals, Python or comparable language skills, and familiarity with machine learning or LLMs. Preferred experience includes agent frameworks, RAG, vector databases, cloud platforms, and containerization.

Build and own Lightspark’s internal AI and data platform, including agent infrastructure, MCP servers, tool integrations, governed data, sandboxed execution, and production pipelines. Partner with Finance, Sales Ops, Compliance, and other teams to replace manual workflows with reliable systems. Extend dbt and cloud data warehouse infrastructure, support monitoring and data reliability, and improve engineering tooling. The role requires production experience with AI agents, LLM APIs, internal platforms, and TypeScript/Node.js or Python.

Designs, builds, and deploys Python-based NLP and machine learning workflows, knowledge graphs, analytical tools, APIs, and collaborative TypeScript applications. Integrates data feeds, supports secure end-to-end pipelines, prototypes solutions in ambiguous environments, and partners with Joint Staff customers in classified SIPRNet and JWICS environments. The role requires adapting technologies to mission-specific analytical and operational needs while providing documentation and technical support.

Build and operate governed production data pipelines in Databricks using medallion architecture, Unity Catalog, data quality controls, lineage, and semantic models. Develop AI agents and retrieval-augmented applications grounded in enterprise data using Mosaic AI and model serving. Integrate enterprise systems through APIs and events, implement security and observability, and own architecture, testing, CI/CD, deployment, and production support.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Develop and operate scalable AI inference solutions for scientific workflows on HPC systems and AI accelerators. Build Python REST services, integrate AI models, optimize inference performance, and support distributed workloads involving batching, scheduling, memory management, and observability. Collaborate with scientific application teams, academic and industry partners, national laboratories, and agencies to advance AI-enabled scientific computing.

Architect, build, deploy, and operate production-grade AI agentic systems and workflows. Responsibilities include backend and full-stack development, agent orchestration, workflow state, retrieval, memory, approvals, failure recovery, data architecture, enterprise integrations, cloud infrastructure, observability, security, and scaling. The role establishes reusable engineering patterns and partners with users to deliver reliable AI products, while addressing production issues involving model behavior, permissions, latency, cost, and consistency.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Design and build real-time, highly concurrent software and cloud-native systems for an AI-powered emergency communications platform. Integrate and deploy LLMs, optimize voice and audio-processing pipelines, and support speech recognition, synthesis, detection, and filtering. Contribute to infrastructure, observability, security, scalability, and fault tolerance. Provide technical leadership through architecture decisions, code reviews, mentoring, and engineering best practices.

Build and operate scalable backend services for connected devices, including device connectivity, telemetry ingestion, health monitoring, command and control, and data pipelines. Collaborate with firmware, mobile, and product teams on APIs and workflows. Use AWS, microservices, streaming systems, observability tools, and AI coding agents to deliver reliable features, participate in incident response, and improve automation and engineering practices.

Design and implement large-scale, AI-driven SD-WAN and AIOps systems. Develop fault-tolerant, real-time, message/event-driven services in C++/Python/Go. Drive feature design, code and design reviews, testing, debugging, optimization, and end-to-end delivery while collaborating with product teams.

Build and maintain mathematical optimization models for network planning, capacity allocation, routing, and freight operations. Develop production AI applications for process automation, document extraction, and decision support. Establish evaluation, validation, monitoring, and regression-testing frameworks for optimization and AI systems. Identify new business opportunities, prototype solutions, quantify impact, and scale successful initiatives. Partner with operations, pricing, sales, finance, engineering, and leadership to communicate trade-offs and deliver reliable analytical systems.

Build and deploy AI solutions for clients, including agentic workflows, RAG systems, structured extraction, OCR and document-processing pipelines, retrieval systems, evaluation harnesses, and lightweight interfaces. Translate ambiguous client problems into production code, drive engagements from discovery through deployment, collaborate with stakeholders, and contribute to internal platform capabilities and engineering practices.

Lead AI transformation across Sales, Marketing, Customer Success, Partnerships, and Revenue Operations. Identify high-value opportunities, design and deploy production AI agents, agentic workflows, automations, integrations, and enterprise solutions, then drive adoption and measure business impact. Partner with functional leaders, make sound decisions about AI suitability, redesign operating models, improve reliability and governance, and mentor colleagues while establishing reusable standards across the organization.

Build and own AI team systems spanning data ingestion, labeling, training and evaluation infrastructure, model deployment, perception integration, and customer-facing applications and APIs. Drive high-impact technical projects, make hands-on architectural decisions, mentor engineers, and support a broad AI software stack. The role is approximately 70% backend and 30% frontend, with focus areas including ML Ops, data pipelines, or computer vision.

Develops production-ready AI, data automation, integration, and workflow solutions for engineering processes in an aerospace and defense environment. Responsibilities include programming, API and database integration, engineering toolchain integration, CI/CD, testing, documentation, workflow analysis, roadmap development, training, and user support. The role collaborates with systems engineers and customer teams and may deploy tools in classified environments.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core, including vector and matrix engines. Own functional features, performance, power, area, timing, and physical design considerations. Develop specifications, optimize RTL, support verification, and collaborate on CPU modeling, design verification, and SoC integration. The role requires deep expertise in high-performance CPU architecture, cache and memory subsystems, SystemVerilog or related HDL technologies, and advanced processor design.

Support ML engineers running large-scale training and inference workloads across Kubernetes, cloud, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, networking, storage, scheduling, performance, and reliability issues. Analyze observability data, guide customers through complex infrastructure problems, support production incidents, and improve platform reliability through automation, tooling, documentation, runbooks, and operational processes. The role partners closely with infrastructure, networking, and platform engineering teams in a hybrid office environment.

Build and operate production machine learning and backend systems for AI data and evaluation products. Own a business vertical end to end, from identifying opportunities and setting direction to delivering revenue-generating products. Partner with customers, product managers, and engineers; drive adoption, customer value, and execution quality. Apply machine learning across multimodal data, LLM evaluation, physical-world data, robotics, audio, video, and related domains.

Lead the technical strategy and architecture for foundation models powering search, recommendations, personalization, and conversational AI across Uber’s Mobility and Delivery products. Own cross-team initiatives involving retrieval, ranking, embeddings, and LLM assistants; guide build-versus-buy decisions; influence product strategy; deliver large-scale ML systems; and mentor senior engineers while connecting model improvements to business outcomes.

Defines the multi-year technical vision and architecture for foundation models supporting search, recommendations, personalization, and conversational AI. Leads high-impact ML platform initiatives across Mobility and Delivery, drives retrieval, ranking, and LLM system design, and makes build-versus-partner investment decisions. Provides principal-level technical leadership, mentors senior engineers, aligns executives across organizations, and connects model improvements to measurable business outcomes at global scale.

Architect, productionize, and scale autonomous generative AI support agents. Lead agentic planning, memory, retrieval, reasoning, policy enforcement, safety, evaluation, multilingual capabilities, and cost optimization. Partner with Product, Design, and Operations to deliver reliable customer-facing automation at global scale. Mentor multiple engineering pods, establish technical strategy, and guide experimentation, rollout, and reliability standards.

Build and scale machine learning and backend distributed systems for Uber AI Solutions. Own an AI product vertical end to end, driving technical direction, customer value, revenue, adoption, and market growth. Partner with product and business leaders, guide strategic customer relationships, make decisions in ambiguous environments, and build high-performing teams. The role focuses on production ML, GenAI and LLM systems, evaluation, multimodal data, and other applied AI domains.

Lead end-to-end delivery of an enterprise cloud data and AI platform. Design data architectures, ingestion pipelines, models, and machine-learning systems; productionize supervised, unsupervised, deep-learning, and generative AI solutions; establish MLOps, explainability, monitoring, and CI/CD practices; and lead distributed engineering teams. Serve as the primary client-facing technical liaison, driving architecture, governance, documentation, UAT, cutover, and production readiness.