Top AI Engineer Jobs

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

As a founding Solutions Engineer, you will lead technical discovery, run POCs, and ensure successful customer integration while collaborating with product and revenue teams.

Lead design and implementation of scalable AI-centric systems for offensive security. Develop agentic AI capabilities for vulnerability identification and automated pentesting, own feature delivery from ideation to production, mentor engineers, run design/code reviews, and collaborate cross-functionally to integrate AI into platform systems.

Design, build, and deploy LLM-powered AI agents and intelligent systems for cloud-native, microservices architectures. Partner with stakeholders to create agent-driven workflows, perform prompt engineering, RAG and vector DB integration, orchestrate agents, ensure CI/CD and code quality, manage technical debt, and benchmark/evaluate models to scale responsible AI across investments technology.

Lead and drive strategy for generative AI projects, develop cutting-edge models, engineer data pipelines, and collaborate across teams to implement AI solutions.

Lead the Generative AI team at Xometry, setting vision and strategy, developing multimodal models, and collaborating with various stakeholders to deliver AI solutions.

Lead the Generative AI team by setting technical direction, developing large-scale models, and mentoring team members while collaborating across departments.

Lead the Generative AI team by setting technical direction, developing models, creating data pipelines, and driving AI strategy across Xometry's marketplace.

Design and implement AI-powered educational software, focusing on generative AI systems, mentor peers, and ensure scalable, secure architecture.

Integrate and maintain commercial AI tools and LLM agents into VLSI design flows, coordinate multi-agent systems, optimize performance and QoR, deploy AI agents and chatbots, preprocess data, evaluate solutions, and train end-users to improve design productivity.

Build and operate AI-native GTM systems that integrate Salesforce, Gong, Snowflake, and proprietary viewership data. Architect MCP servers, RAG pipelines, and agentic workflows using Python/JavaScript and Claude. Design eval frameworks, monitor production LLMs, run A/B tests, automate seller workflows, and enable revenue teams with documentation and training to drive pipeline and revenue impact.

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.

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.

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.

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.

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

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.

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.