Top AI Engineer Jobs

Leads AI adoption and transformation across Finance by identifying use cases, building AI agents and workflow automations, developing reusable AI assets, and delivering training programs. Partners with Finance and technology leaders to drive enterprise adoption, citizen innovation, and workforce capability development. Measures productivity gains, adoption, proficiency, and business value while ensuring solutions meet governance, security, privacy, compliance, and Responsible AI standards.

Lead engineering and deploy scalable AI agents and agent orchestration at scale. Prototype GenAI solutions, design and operationalize distributed batch/real-time data flows, implement memory/RAG and vector DBs, integrate tool-calling/APIs, build calibration and guardrails, drive cloud adoption, and collaborate with global teams.

Leads the architecture, development, deployment, and operation of enterprise AI Ops solutions, including RAG pipelines, agentic workflows, and AI-powered applications. Improves SRE practices, leads incident response, automates cloud infrastructure with Terraform and GitHub Actions, and ensures reliability, scalability, security, and responsible AI. Designs and operates software using Python and Node.js, manages Kubernetes environments, mentors engineers, and leads globally distributed technical teams.

Lead development of AI-powered features and scalable agent infrastructure for Hex. Partner with Research on model prompting, build experimentation tooling, and deliver performant full‑stack user experiences using web and backend systems. Mentor engineers and drive major product initiatives.

Lead architecture, design, and delivery of Klaviyo's Customer Agent conversational AI platform. Drive technical strategy, build scalable AI-enabled features, establish observability and reliability practices, mentor engineers, and coordinate cross-functional initiatives to deliver production agentic systems for millions of users.

Lead architecture, design, and implementation of a scalable conversational AI platform (Customer Agent). Drive technical strategy for LLM-powered agentic systems, retrieval, orchestration, memory, evaluation, and reliability. Ship AI product features, establish observability/testing/operational best practices, lead cross-functional initiatives, mentor engineers, and evaluate emerging AI technologies to deliver reliable customer-facing experiences.

Design and implement algorithms for image processing and object tracking for spacecraft, integrating with flight software and conducting simulations.

The Principal AI/ML Engineer will lead the AV ML Infra team, overseeing scalable ML infrastructure, project ownership, collaboration, and team mentorship in AI and ML innovation at GM.

Develop ML and multimodal AI systems that integrate neurophysiological, behavioral, and sensor data for cognitive state estimation and decision support. Build algorithms and prototypes, apply cognitive science to improve explainability and human-AI collaboration, contribute to proposals/publications, and demonstrate capabilities for mission-driven R&D.

As a Senior Machine Learning Engineer, you will manage the entire lifecycle of safety-critical ML perception models, optimize performance, and ensure reliable deployment on embedded systems.

As a Staff AI/ML Engineer, you'll enhance developer productivity, mentor engineers, drive best practices, and implement scalable solutions for AV tools and services.

Design, build, and operate Zocdoc's internal developer platform, CI/CD pipelines, and developer tooling to improve developer productivity, safety, and reliability. Collaborate across teams to deliver platform investments end-to-end, drive adoption, advance AI-assisted development capabilities, mentor engineers, and ensure platform patterns meet security and compliance requirements.

Lead design and build of New York Life's enterprise AI platform and agentic solutions. Work across cloud-native infrastructure, model and agent lifecycle, RAG knowledge systems, multi-agent orchestration, MLOps/LLMOps, governance, CI/CD, observability, and production operations. Partner with security, legal, and architecture to embed responsible AI and deliver production-ready, compliant AI systems.

Develop, train, and deploy machine learning and generative AI models in production. Focus on engineering aspects: scalable software, MLOps/DevOps integration, CI/CD pipelines, monitoring (e.g., New Relic), and automation frameworks to ensure model reliability and performance.

Design, build, and operate scalable services and distributed systems supporting agentic AI capabilities. Lead system, API, and data architecture; integrate AI/ML solutions across development and production environments; establish standards for reliability, security, testing, observability, and compliance; partner cross-functionally on technical direction; and mentor engineers while building reusable AI tools, orchestration layers, and safeguards.

Design, build, and scale AI engines, conversational assistants, context-aware agents, and workflow automations for go-to-market teams. Own the GTM AI roadmap, trusted data and knowledge architecture, AI governance, production monitoring, adoption, and value realization. Partner with Sales, Marketing, Revenue Operations, Product, and Systems leaders to translate business needs into scalable AI solutions and measure their impact.

Architect, build, and operationalize production-grade generative AI and ML systems across Snowflake, Databricks, and Azure ML. Implement RAG, vector search, agentic AI, and API-based model services; create CI/CD, MLOps/LLMOps practices, observability, and governance. Mentor engineers, troubleshoot inference and drift issues, and integrate AI services into regulated healthcare products ensuring security and compliance.

Lead and develop a team of Applied AI Engineers while remaining hands-on in production code. Own technical outcomes, architecture decisions, engineering standards, customer technical relationships, hiring, and delivery across ambiguous enterprise environments. Partner with engagement and growth leaders, provide deliberate coaching and feedback, support customer deployments, and translate field insights into improvements for Mosaic, the company’s AI deployment toolkit.

Develop AI-enabled software and production applications that improve cyber operations. Partner with mission operators to identify challenges, rapidly prototype solutions, and apply Python, software engineering, AI, data analytics, cybersecurity, LLMs, Splunk, Kubernetes, Rancher, Airflow, and DevSecOps practices to mission objectives.

Lead AI technical strategy and roadmap across product portfolio. Drive design, development, and production deployment of large-scale ML and generative AI models (including LLMs), mentor teams, align stakeholders, mitigate risks, and ensure timely delivery of AI capabilities.

Build and operate production AI systems from prototype through deployment and monitoring. Translate business workflows into reliable LLM-powered tools, develop prompts, retrieval logic, evaluation harnesses, and integrations, and establish governance and deployment patterns. Evaluate vendors and AI platforms, contribute to the technical roadmap, and partner with stakeholders across the company. The role requires enterprise-aware engineering, clear communication, comfort with ambiguity, and in-office work in Houston at least three days weekly.

Develop and productionize GenAI applications for federal-sector customers, including RAG, multi-agent systems, Text2SQL, and fine-tuning solutions. Advise customers, lead production rollouts, present technical concepts, collaborate with product and engineering teams, and influence the AI product roadmap. The role requires production machine learning deployment experience, strong data science expertise, customer-facing communication, U.S. citizenship, Secret clearance eligibility, and periodic customer travel.

Embed with customers to understand workflows, design and deploy tailored conversational AI solutions, and build supporting infrastructure, APIs, integrations, monitoring, and feedback loops. Translate business needs into technical architectures, identify reusable deployment patterns, influence the product roadmap, and operate autonomously in ambiguous environments while collaborating with technical and non-technical stakeholders.

Develop, optimize, and deploy audio and multimodal machine learning models on embedded and edge devices. Build real-time inference pipelines, convert models to efficient C/C++ implementations, and optimize latency, memory, SRAM, and power across MCUs, DSPs, NPUs, and accelerators. Collaborate with researchers, DSP experts, firmware engineers, and hardware teams to deliver production-ready embedded AI systems using model development, quantization, profiling, and on-device runtimes.

Design and build agent-based AI features that integrate across BigCommerce and partner systems. Connect agents to MCP servers, enable natural-language store management actions, integrate assistants into the control panel, collaborate with designers and NLP experts, prototype POCs, and contribute to tooling for LLM fine-tuning, prompt engineering, and safe action execution.