Top AI Engineer Jobs

Design and build scalable EDA flows and AI/LLM-driven automation for 3DIC, interposer, and RDL systems. Implement flow-generation, debugging assistants, root-cause analysis, and platform integration using C++, Tcl, Python, and Java. Collaborate with engineering teams to validate flows, automate constraint-driven designs, and address large-scale, high bump-count and multi-region routing challenges to deliver production-quality solutions.

Design, develop, and deploy machine learning models and AI platforms. Preprocess and analyze large datasets, implement scalable cloud-based solutions, integrate models into applications, monitor and optimize model performance, document code and processes, and collaborate with cross-functional teams to deliver AI solutions.

As an AI Lead Engineer, you will architect and implement Generative and Agentic AI capabilities, optimize performance, and lead a team in developing scalable AI solutions while mentoring others.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

As an Applied AI Engineer, you'll develop and deploy AI solutions, collaborate with customers, and optimize production systems using Python and SQL. Your role involves driving customer impact through innovative AI technologies.

Design and build scalable, secure infrastructure and core clean-room features for multi-party data collaboration. Implement developer APIs, AI-enabled capabilities and agents, partner with product and cross-functional teams to deliver production systems, and mentor peers to ensure technical excellence and privacy-preserving analytics.

Lead enterprise AI programs, mentor engineers, define quality metrics, and deliver scalable AI solutions while collaborating across teams and advising stakeholders.

Drive pre-sales for NICE Customer Service Automation by designing technical solutions, delivering demos, evaluating customer infrastructures, responding to RFIs/RFPs, and partnering with sales, product, and engineering to apply conversational AI, analytics, and digital engagement to CX problems.

Lead design and orchestration of AI-first agent workflows to produce application code, infrastructure, and tests. Architect full-stack systems and MCP servers, validate AI-generated outputs for correctness, security, and performance, build verification pipelines and observability, mentor teams, and evolve AI-First SDLC and governance practices.

This role involves designing, developing, testing, and maintaining software solutions, collaborating with various teams while focusing on scalable and reliable engineering.

Design and own AI-agent-driven test workflows and the automated verification framework. Build test context, select models and tooling, validate AI-generated code and tests, operate test automation across UI/API/mobile/data, ensure security and compliance, mentor teams, and continuously improve agent orchestration and observability.

Lead design and build Benepass's internal AI platform: agents, tool interfaces, retrieval, evals, workflow runners, and observability. Accelerate the engineering SDLC with AI-assisted coding, CI/CD integrations, quality automation, and testing. Ship reusable primitives and agentic workflows, enable company-wide automation, set standards and guardrails, and partner across teams to drive adoption and measurable outcomes.

Lead the design and build of an internal, company-wide AI platform: agents, retrieval/RAG, evals, workflow runners, and CI/CD integrations. Accelerate engineering SDLC with AI-assisted coding, testing, and quality gates, then generalize platform primitives across non-engineering teams for automation, observability, and safe production use.

Design and build cloud-native AI solutions on Oracle Cloud Infrastructure: develop AI agents, integrate mission data with LLMs (RAG/MCP), deliver production geospatial systems, build Node.js dashboards tied to Oracle Databases, and ensure secure, scalable OCI deployments for defense clients.

Design and implement cloud-native data pipelines and metadata readers on OCI, build AI agents and integrate mission data with LLMs using RAG/MCP, develop production geospatial solutions, create Node.js dashboards connected to Oracle Databases, and ensure secure, scalable cloud architectures for defense customers.

Lead integration and optimization of GPU-accelerated ML for robotic systems: profile and tune models and kernels, design software interfaces across edge, firmware, OS, and cloud, develop Linux kernel and driver components, build containerized GPU runtimes, drive system architecture and roadmaps, and collaborate cross-functionally to deliver validated, high-performance medical device solutions.

Build and prototype agent-based AI applications and workflows, connecting LLMs, prompts, tools, and APIs. Rapidly prototype then harden code, execute CI/CD and testing, write docs/runbooks, participate in reviews, and apply secure-by-design and responsible AI practices.

Design, build, and deploy production-grade agentic applications and multi-agent workflows that automate IT and business processes. Convert prototypes into hardened services, integrate LLMs and enterprise data sources, implement CI/CD and observability, author runbooks and docs, lead reviews and threat modeling, and apply secure-by-design and responsible AI practices.

Design, build, and maintain TypeScript AWS Lambda REST APIs and cloud infrastructure for conversational AI voice agents. Implement and optimize AI agents, prompt engineering, telephony integrations (Twilio), storage, and monitoring. Follow TDD, write unit/integration tests, use AWS CDK, and collaborate with stakeholders on scalable AI solutions.

Design, develop, and deploy generative AI, LLM-powered, and agentic systems for federal clients. Lead engineering best practices, implement secure integrations, enable cloud/DevSecOps pipelines, monitor production performance, and mentor engineers while collaborating across data, cloud, and stakeholder teams.

Design and implement agentic systems using LLMs and agent frameworks, build multi-agent workflows and RAG pipelines, optimize scalable agent architectures, collaborate on experiments and feature integration, and ensure quality through reviews, testing, and documentation.

As an AI Software Engineer, you will develop Generative AI solutions, manage full-cycle development, implement MLOps pipelines, and ensure compliance with AI standards.

Design, build, and operate enterprise AI/ML platform capabilities (AIOps/MLOps/LLMOps). Develop GenAI features, experiment with models and services, and architect end-to-end solutions. Collaborate with data, cloud, and business teams, drive engineering execution, promote best practices, and mentor engineers. Ensure platform reliability, monitoring, and scalable deployments across cloud environments.

Design, simulate, and characterize RF/analog/mixed-signal ICs for space and ground; derive RFIC specifications; perform layout, packaging and parasitic modeling; validate prototypes and radio performance; develop test equipment and support production testing and qualification.

Design, build, and maintain production-grade agent harnesses and platform capabilities for generative AI applications. Lead architecture and delivery, improve CI/CD, testing, observability, and security, mentor engineers, and collaborate with stakeholders to enable scalable, enterprise-grade agent runtimes and integrations.