Top AI Engineer Jobs

Build and maintain production AI/ML applications, backend services, data pipelines, and machine learning models. Develop agentic AI systems, LLM and RAG solutions, and MLOps capabilities including monitoring, versioning, A/B testing, and retraining. Collaborate with product, software, platform, and data teams to deliver reliable, explainable, and performant AI solutions for financial markets.

Design and implement advanced AI and agentic systems for intelligent instruments and autonomous labs. Lead cross-functional AI projects for imaging and automation, translate product concepts into AI architectures, drive AI software stack innovation, and ensure measurable product KPIs and roadmap alignment.

Lead design, prototype, and deploy generative AI solutions for healthcare. Own end-to-end projects, collaborate with product and design, experiment with LLMs/RAG/agents, and build scalable full-stack applications and intelligent agents to improve member experience and operations.

Lead the architecture, development, deployment, and governance of production-grade agentic AI systems supporting investment research, quantitative analysis, portfolio management, and trading. Mentor engineering teams, establish AI engineering standards, build agent-driven workflows, manage technical debt, and ensure responsible scaling across cloud-native platforms. Partner with investment and business stakeholders to translate requirements into scalable solutions that deliver measurable value.

Design, build, and optimize LLM- and RAG-powered applications for government and enterprise missions. Develop document ingestion, embeddings, vector retrieval, LLM orchestration, traditional machine learning models, APIs, and cloud-native containerized workloads. Apply software engineering, machine learning, MLOps, security, and model evaluation practices to deliver reliable production systems. Collaborate with data engineers, architects, and mission subject-matter experts to create scalable solutions.

Build and deploy AI-enabled workflow automations and agentic systems across the Office of the CEO, Capital Markets, Finance, and Operations. Integrate LLMs and foundation models with BI/data infrastructure, prototype and scale production-ready copilots and RAG solutions, and document/enable reusable templates while ensuring data security, governance, and stakeholder adoption.

Build and maintain internal AI-powered applications using Retool, Python APIs, and enterprise LLMs. Integrate Snowflake, dbt, source-system APIs, and operational data while implementing HIPAA-compliant PHI controls, role-based access, masking, audit logging, and data minimization. Develop and evaluate prompts, design workflow-focused user experiences, measure adoption, gather feedback, and continuously improve tools supporting RCM, billing, authorization, and clinical operations.

Lead technical evaluations, demos, and proof-of-concepts of the Domino platform for US federal and defense customers. Design solution architectures meeting federal security/compliance (FedRAMP, IL5), build reusable demo assets, and drive post-POC adoption working with Sales, Customer Success, and Solutions Architects to increase win rates and accelerate deployment.

Build and lead production AI agents for research, software, hardware, data, and operations workflows. Establish reusable agent infrastructure for models, tools, orchestration, context, memory, retrieval, permissions, approvals, and long-running execution. Develop trusted data integrations, evaluations, regression testing, observability, monitoring, and failure-analysis systems. Evaluate frontier models and agent-development tools, set architecture standards, review code, and advise leadership on strategic analytical initiatives.

Design and build agentic AI features by integrating LLM APIs into product workflows. Write well-tested TypeScript across the stack, implement tool invocation and multi-step reasoning flows, collaborate with Product and Design, and improve reliability and performance of agentic systems while documenting findings.

Design, build, deploy, and operate production-grade AI agents and multi-agent systems. Implement RAG, agent orchestration, copilots, observability, memory patterns, and enterprise integrations while ensuring governance, reliability, and onshore/offshore delivery.

Build and own full-stack AI product features from concept through launch, adoption, and iteration. Translate user, partner, product, and enterprise requirements into reliable experiences for AI chat, search, research, workflow automation, administration, and integrations. Collaborate across engineering, product, design, enterprise teams, and open-source contributors. Evaluate AI tooling, make technical and product tradeoffs, write maintainable tested code, measure product adoption and reliability, and establish reusable patterns for an enterprise-ready open-source platform.

Build and productionize applied AI and machine learning capabilities for Crucible, including LLM workflows, retrieval, tool use, orchestration, evaluation, and model-serving systems. Integrate optimization and forecasting models into reliable products, select models for constrained cloud, air-gapped, and edge environments, and establish technical standards for production AI. Collaborate with manufacturing, supply chain, data science, software engineering, and product teams to deliver measurable operational value.

Build and operationalize production-grade AI applications, including AI agents, RAG pipelines, intelligent search, automated reporting, and decision-support tools. Integrate AI with enterprise systems, databases, and APIs using Python, C#/.NET, and TypeScript. Implement responsible AI guardrails, LLMOps monitoring, evaluation, security, and governance. Mentor developers, assess emerging AI technologies, and translate business needs into reliable technical solutions.

Build and deploy production-grade AI agents within enterprise customer environments. Define solutions around business workflows, design and run evaluations, improve agent performance, and own end-to-end delivery. Collaborate with Product and Platform Engineering, establish reusable agent frameworks and engineering practices, develop customer domain expertise, and surface deployment insights for product strategy. The role requires strong backend-focused full-stack engineering, production AI experience, systems thinking, communication, and comfort working with senior enterprise stakeholders.

Design and implement SystemVerilog RTL and microarchitectures for AI-optimized memory systems; perform synthesis and PPA analysis; document microarchitecture; evaluate tradeoffs and drive implementations from specification through silicon.

Lead and execute integration, assembly, and acceptance testing of spacecraft hardware in lab and cleanroom environments. Develop and run AI&T test procedures, maintain test logs and reports, use GSE and test equipment, troubleshoot systems, collaborate with engineers and customers, and support launch operations.

Build and maintain computer vision and machine learning pipelines for automated device inspection, grading, and quality assessment. Responsibilities include data preparation, feature engineering, model training and evaluation, production Python development, monitoring, annotation workflows, documentation, code reviews, and collaboration with product and operations teams. The role supports the full ML lifecycle and offers increasing ownership under senior engineering guidance.

Lead architecture and development of production-grade agentic AI and multi-agent systems for reliability engineering. Design autonomous agents, manage AI infrastructure and deployment, ensure observability/security/guardrails, automate remediation, evaluate vendors and models, and produce secure, high-quality production code to improve system reliability and engineering productivity.

Own, build, and optimize Spark-based production data pipelines and ML data lake to support recommender systems and personalization. Scale cluster configurations on GCP Dataproc, ensure data quality and accessibility, support ML engineers for model development, resolve performance bottlenecks, and collaborate on platform architecture to deliver reliable data products that drive business value.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

Lead design and delivery of AI-driven product solutions across the commerce platform. Own high- and low-level architecture, implement scalable RAG/LLM systems, select AI infrastructure, mentor teams, and collaborate with product and engineering leadership to execute AI strategy and rapid experimentation.

Build and improve a realtime multimodal agent (Aidan) across voice, vision, browser interaction, and a self-improving context graph. Improve runtime latency, perception, skills extraction from demos, action planning for live browser use, and automated improvement pipelines that learn from suboptimal actions.

Build and own full-stack product features for an AI-powered biopharma documentation platform. Develop React and TypeScript interfaces, backend services, APIs, data models, and production deployments. Design scalable systems for complex enterprise workflows, create AI-native scientific and regulatory content experiences, collaborate cross-functionally, and make pragmatic architectural decisions in a fast-paced startup environment.

Own and evolve ShiftKey's AI infrastructure: production retrieval/indexing, agentic platforms on AWS, graph-based multi-hop retrieval design, platform APIs, reference agents, and cost/observability guardrails for regulated (HIPAA) workflows.