Top AI Engineer Jobs

Deploys and optimizes semiconductor EDA-to-ATE conversion flows using Velocity and related tools. Responsibilities include developing STIL, WGL, EVCD, IJTAG, and SSN conversion methodologies; supporting Advantest platforms; debugging timing, protocol, and pattern issues; creating Python and Java automation utilities; applying AI-assisted diagnostics; collaborating with customer and internal engineering teams; and delivering training and technical workshops.

Build customer-facing agentic AI experiences, including chatbots, embedded assistance, MCP integrations, and automated reconciliation workflows. Partner with product teams, domain experts, customers, and engineers to identify valuable AI applications across fund-management software. Design reliable multi-step agent systems, evaluate model outputs and failure modes, and ship production software while shaping product direction in a fast-moving environment.

Leads the end-to-end architecture, deployment, operation, and scaling of AI compute infrastructure for orbital data centers. Owns GPU/CPU, networking, storage, orchestration, cloud-platform, spacecraft network, testing, and qualification strategies. Builds and manages the compute payload engineering organization, leads hardware/software co-design, and makes supplier, component, technology-partner, and make-or-buy decisions. Requires extensive hyperscale, HPC, AI infrastructure, GPU cluster, hardware, networking, storage, and team leadership experience.

Design and implement machine learning research systems for generative models, causal inference, planning, and multi-objective optimization. Build training and inference pipelines, dataset loaders, experiment tracking, versioned datasets, and containerized model components. Develop synthetic data campaigns, translate research prototypes into tested production code, present findings, document results, and collaborate with RF, DSP, formal methods, academic, and government stakeholders.

Design, develop, deploy, and maintain AI/ML models and pipelines. Collaborate with cross-functional teams, implement MLOps and CI/CD, monitor model performance, investigate incidents, ensure compliance with security/privacy/ethical requirements, and document model architectures and procedures.

Provides senior systems engineering leadership for integrating AI/ML capabilities into MQ-9 unmanned aviation systems. Develops requirements, architectures, implementation strategies, roadmaps, trade studies, technical evaluations, and engineering analyses. Oversees AI/ML data management, model validation, integration, testing, and deployment while coordinating government, industry, and engineering stakeholders. Supports defense acquisition, fielding, sustainment, deficiency investigations, and executive-level technical decision-making within the NAVAIR program environment.

Develop and deploy AI agents using Gracenote MCP servers, lead technical pre-sales and proofs of concept, architect enterprise integrations, guide customer implementations, and advocate for product improvements. The role requires translating complex AI architectures into business solutions, supporting API integrations, and engaging stakeholders from executives to developers. Travel to customer sites globally may reach 40%.

Design, develop, test, evaluate, deploy, and maintain AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Process structured and unstructured data, build ETL pipelines, conduct experiments, measure performance, deploy models in cloud environments, document findings, brief stakeholders, educate staff, and provide AI/ML subject matter expertise.

Build and own a real-time conversational voice AI platform for automotive customers. Responsibilities include developing production systems across speech, LLM, telephony, and event-driven AWS services; optimizing latency and reliability; implementing observability, failover, infrastructure as code, CI/CD, and integrations; and leading technical decisions, engineering standards, knowledge sharing, and mentoring.

Build and own Android growth and monetization experiments across subscriptions, advertising, purchases, and upgrade flows. Partner with product, design, data science, and analytics to instrument experiments, evaluate results, and ship reliable experiences at consumer scale. Use AI-native workflows for specification, coding, testing, and review; improve mobile engineering practices; contribute to architecture and code reviews; participate in on-call; and mentor engineers.

Design, build, deploy, and support enterprise AI agents, copilots, intelligent workflows, and lightweight applications. Integrate AI solutions with APIs, Microsoft 365, SharePoint, CRM systems, and business data while ensuring security and scalability. Translate business needs into practical solutions, prototype emerging technologies, improve agent performance, and collaborate with stakeholders to drive user adoption and measurable business value.

Build and maintain backend infrastructure and member-facing generative AI features that deliver personalized health and performance insights. Collaborate with Product, Design, Analytics, and Data Science teams to define requirements and deliver scalable solutions. Develop reliable software using Java, Kafka, AWS, and PostgreSQL; design RESTful APIs and relational databases; optimize performance; and maintain observability and monitoring systems.

Design, develop, and deploy cloud-hosted distributed applications supporting scientific research publishing. Collaborate with stakeholders and Agile, DevOps, and cross-functional teams; contribute to architecture, performance, stability, and delivery improvements. Apply full-stack Java and JavaScript/TypeScript development, testing, monitoring, documentation, and cloud technologies. Mentor team members, promote engineering best practices, and help explore AI-based solutions.

Design, develop, and deploy cloud-hosted applications supporting scientific research publication. Collaborate with stakeholders and Agile, DevOps, and cross-functional teams; contribute to architecture, performance, monitoring, testing, documentation, and delivery improvements. Mentor team members, apply industry best practices, support continuous improvement, and develop solutions using Java, Spring, JavaScript or TypeScript frameworks, cloud technologies, CI/CD tools, and emerging AI capabilities.

Build and own Python-based performance and power modelling frameworks for photonic AI hardware. Model concurrency, event-driven behavior, compute, memory, interconnects, and accelerator blocks; characterize AI workloads against hardware and RTL results; consume MLIR and compiler outputs; analyze simulation traces and benchmarks; validate power models; maintain CI/CD and simulation flows; and guide architecture and compiler decisions while developing future team members.

Design, train, deploy, monitor, and optimize AI/ML models powering a cloud-based voice agent platform. Responsibilities include STT, NLU, and TTS development; MLOps pipelines; AI API services; inference and latency optimization; frontend prototypes and dashboards; research, experimentation, vendor evaluation, documentation, and code quality. The role requires collaboration with backend, frontend, and infrastructure teams.

Lead design and implementation of enterprise-scale AI systems including RAG architectures and autonomous agents; lead full-stack development across AI orchestration, APIs, microservices and web UIs; set technical strategy, mentor engineers, champion MLOps/LLMOps, ensure security/ethical compliance, and collaborate cross-functionally to deliver scalable AI products.

The Data & AI Engineer III will lead cloud data engineering solutions, develop data infrastructure, establish architectures, mentor team members, and secure data around cloud systems.

Own the generation layer of Document Intelligence by designing prompts, schemas, validators, multimodal context pipelines, and quality evaluations. Build production LLM features that transform video, audio, PDFs, images, OCR, transcripts, and keyframes into validated maintenance entities. Balance model quality, cost, and latency, monitor regressions, and collaborate with processing and domain engineering teams.

Lead design, code, and architect scalable hybrid-cloud solutions for transactional and analytics workloads. Implement and review high-quality code, CI/CD pipelines, and AI/GenAI systems. Collaborate with development leads and stakeholders, present technical updates, prioritize work, and mentor team members to ensure robust, production-grade delivery and monitoring.

Build and lead the Site Reliability Engineering function across cloud, colocation, on-premises labs, and customer-facing services. Establish SLOs, observability, incident management, IaC automation, self-healing infrastructure, FinOps, capacity planning, and shared-storage architecture. Lead SRE and data center teams, hire and develop engineers, manage reliability and technical direction, and serve as the senior incident escalation point.

Owns Azure-based AI platform infrastructure, deployment automation, CI/CD pipelines, testing, observability, security controls, and release management. Builds containerized, infrastructure-as-code environments; implements monitoring, incident response, disaster recovery, and high availability; optimizes cloud performance and costs; and establishes secure platform engineering standards for AI-enabled applications. Collaborates with architecture, security, infrastructure, and development teams to scale reliable enterprise software delivery.

Build and operate the multi-tenant platform layer for a GPU cloud service. Responsibilities include Kubernetes-based orchestration, tenant isolation, platform APIs and tools, GPU cluster automation, infrastructure as code, reliability and observability partnerships, customer issue support, environment templates, rollout tooling, architecture reviews, and reducing operational toil.

Design, develop, and maintain enterprise AI applications, including conversational assistants, copilots, autonomous agents, RAG solutions, and workflow automation. Build scalable APIs and microservices integrating foundation models and enterprise systems. Apply prompt and context engineering, memory management, observability, governance, and performance optimization. Collaborate with cross-functional teams to identify automation opportunities and participate in Agile development, code reviews, CI/CD, and DevSecOps.

Design, develop, and apply AI and machine learning models for mission-critical defense applications. Build multimodal sensor, computer vision, NLP, and large language model solutions using Python and ML frameworks. Analyze large datasets, implement computational models and algorithms, collaborate with software engineers in agile teams, transition research into production-ready software, support proposals and technical reports, and present work to customers and professional audiences.