Top AI Engineer Jobs

Build foundational systems for AI models across data modalities, develop agentic systems and specialized models, and implement reinforcement learning and active inference solutions. The role involves hands-on architecture and product development, requiring Python backend expertise, cloud deployment knowledge, DevOps practices, and the ability to contribute across the technical stack in an early-stage startup.

Design, build, deploy and operate production-grade generative AI and ML solutions (LLMs, RAG, vector search) on cloud-native architectures. Ensure code/model quality, observability, governance, compliance and performance; collaborate with cross-functional teams, mentor engineers, and contribute reusable frameworks and responsible-AI controls.

Leads development of AWS-native software engineering and automation solutions, including IaC, CI/CD, provisioning, observability, and lifecycle orchestration. Builds secure, resilient agentic AI integrations using LLMs and cloud services, improves operational stability, evaluates architectures and vendors, and promotes AI-assisted engineering practices. Mentors teams, establishes validation and responsible AI standards, and leads software engineering communities of practice.

Own technical relationships with strategic enterprise customers throughout Moveworks implementations. Design AI agent solutions, enterprise integrations, workflows, and automations using APIs, iPaaS tools, cloud functions, and scripting. Advise customers on agentic AI roadmaps, coordinate cross-functional delivery, develop reusable capabilities, and mentor engineers. The role combines solution architecture, hands-on implementation, technical consulting, executive communication, and up to 25% travel.

Design, build, and operate production AI automations and agentic workflows for Toast’s customer care systems. Integrate LLMs with Salesforce Service Cloud, Amazon Connect, and workflow platforms; develop autonomous actions with human oversight; test, monitor, and improve system reliability, accuracy, latency, and cost. Collaborate with software engineers, Salesforce developers, and care operations stakeholders to translate workflows into scalable intelligent solutions.

Build AI applications, agents, integrations, and automated workflows that transform technical information into customer documentation. Partner with the Documentation team to use LLMs, agentic AI, and automation to improve content creation, maintenance, publishing, and delivery. Reduce manual effort, increase access to technical knowledge, and identify additional customer-facing workflows where AI can improve efficiency and experience.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.

Build backend services, ontology models, retrieval and evaluation systems, and production LLM workflows for legal drafting and review. Own features end to end across PostgreSQL, OpenSearch, graph databases, and AWS, while improving performance and scale. Work directly with law firm customers alongside senior forward deployed engineers, contribute to TypeScript and React interfaces, and translate vague customer problems into data models, retrieval improvements, or software fixes.

Architect and build reliable, scalable AI platform services supporting model training and inference across hybrid-cloud environments. Develop reusable APIs, SDKs, tooling, and reference architectures; establish observability, resilience, security, automation, and cost-optimization standards. Partner with engineering and product teams to deliver platform capabilities, meet reliability targets, resolve production incidents, and drive remediation. Mentor engineers and promote responsible, secure use of AI-assisted development tools.

Leads architecture and hands-on development of scalable ML and generative AI platforms. Builds Python APIs and SDKs, Databricks data and ML workflows, agentic AI capabilities, model integrations, infrastructure automation, and CI/CD pipelines. Ensures reliability, monitoring, security, performance, and cost efficiency while mentoring engineers, partnering with data science and product teams, and establishing AI-assisted engineering quality standards.

Own the architecture and technical direction of Square’s go-to-market technology stack. Design, build, and operate production AI agents and automated workflows that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations; establish AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering teams to translate business needs into scalable solutions, guide integrations and build-versus-buy decisions, and mentor engineers through influence and technical leadership.

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Build and deploy autonomous AI agents and GenAI solutions at scale. Design and operationalize distributed data flows, agent orchestration, memory/RAG systems, and integrations with LLM APIs. Collaborate globally, prototype state-of-the-art ML/GenAI technologies, and ensure scalable, reliable cloud-native architectures.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Own the architecture and technical direction of Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish reliable and secure AI evaluation practices, make build-versus-buy decisions, and partner with Sales, Product, Data, and Engineering. Remain hands-on across design, implementation, testing, deployment, and operations while mentoring engineers and influencing senior stakeholders.

Own the architecture and technical strategy for Square’s go-to-market technology stack. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish AI reliability and security guardrails, define evaluation metrics, and guide build-versus-buy decisions. Partner with Sales, Product, Data, and Engineering leaders while mentoring engineers and remaining hands-on across design, implementation, testing, deployment, and operations.

Design, build, tune, and launch AI agents and full-stack automations for U.S. federal customers. Architect integrations with enterprise systems while meeting federal security, compliance, procurement, and ATO requirements. Advise customer stakeholders, coordinate cross-functional delivery, support product rollouts, synthesize customer feedback, create reusable solution templates, and upskill agency teams. The role requires hands-on API and LLM systems expertise, customer-facing communication, technical leadership, and up to 25% travel.

Lead design, build, deploy, and support foundational AI systems and products (LLM training/inference, similarity search, guardrails, evaluation, observability). Optimize large-scale AI performance and collaborate with cross-functional teams to produce responsible, scalable AI solutions.

Develop advanced personalization and recommendation systems for digital grocery shopping. Build, deploy, monitor, and maintain machine learning pipelines and microservice APIs supporting production inference. Apply natural language processing, generative AI, large language models, agentic AI, recommendation models, deep learning, transformers, and classical machine learning techniques to real-world business problems. Evaluate and tune models, communicate technical results, and contribute to scalable AI solutions.

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.

Lead a team of ML engineers building and operating scalable risk features, data pipelines, supervised and unsupervised models, LLM solutions, and explainability systems for financial crime monitoring. Responsibilities include productionizing ML applications, monitoring drift and bias, optimizing performance, developing Databricks pipelines, and collaborating with product, SRE, compliance, and operational teams.

Build and ship full-stack AI features using Ruby on Rails and React, including AI agents, RAG systems, evaluations, observability, and LLM inference infrastructure. Design experiments and A/B tests, optimize production performance and reliability, and collaborate with leaders and designers to iterate rapidly on customer-facing solutions.

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.

Designs and operates Kubernetes clusters on AWS, builds scalable and secure AI runtime infrastructure, and develops tooling for developer productivity and automated development workflows. The role improves platform reliability, supports long-running tasks on cost-efficient infrastructure, partners with application, security, and data teams, participates in incident response, and provides technical leadership and mentorship across the Platform organization.

Build and scale WHOOP AI platform systems and member-facing experiences across the frontend and backend. Design scalable web applications, APIs, and intuitive interfaces; contribute to agent orchestration architecture; translate business requirements into reliable solutions; write tested code; and monitor, debug, and improve production performance and stability. Collaborate with product managers, designers, data scientists, and other cross-functional partners in a fast-paced environment.