Top AI Engineer Jobs

Define the technical vision and architecture for enterprise AI products, including multi-agent systems, tool-use frameworks, NL-to-SQL engines, RAG, and LLM reliability infrastructure. Partner with Product, Engineering, and Research leadership to bring AI capabilities to production scale. Design reusable context-engineering patterns, mentor senior engineers, lead high-stakes technical initiatives, and drive experimentation and technical excellence.

Build and operate production AI agent features for Plane, including agent workflows, MCP servers, retrieval systems, customer-facing interfaces, evaluation harnesses, tracing, permissions, and audit models. The role requires shipping reliable LLM-backed features to real users, managing tool calling and failure recovery, measuring quality and performance, and partnering with engineering and product teams to deliver production surfaces.

Owns the integration and testing manufacturing line for AI satellite compute hardware. Responsibilities include managing test infrastructure, designing tooling, assessing manufacturability, integrating vehicles, optimizing test operations, and investigating non-conformances. The role requires collaboration with engineers and technicians, hands-on work in production environments, test planning, configuration management, and occasional travel to SpaceX sites.

Designs and commissions factory automation systems for satellite AI hardware manufacturing. Responsibilities include PLC programming, robotic and conveyor controls, HMI development, electrical system design, safety compliance, machine commissioning, production-rate validation, troubleshooting, and operational handoff. The engineer also develops schedules and budgets, supports electromechanical integration, and ensures compliance with industrial safety and electrical standards.

Design, build, and operate scalable software, cloud services, IoT workflows, microservices, and device software for digital dentistry and 3D printing. Own features end to end from requirements through observability, improve reliability, performance, and cost, and collaborate with technical leaders, product managers, and global engineering teams. Integrate AI and algorithms across native applications and cloud systems while maintaining strong standards for scalability, testability, documentation, and operational excellence.

Lead the vision and strategy for BNY's developer-facing AI products, collaborating across teams to enhance the developer experience and innovation in software engineering.

Design, build, and optimize Python backend services, APIs, integrations, and data pipelines supporting intelligent agent workflows. Implement LLM, embeddings, RAG, and vector database solutions using AI/ML frameworks and external APIs. Improve deployed agents through prompt engineering and fine-tuning, while contributing to testing, CI/CD, monitoring, architecture, documentation, and enterprise deployment reliability.

As an AI Software Engineer, you'll design, develop, and support AI applications, optimizing integrations and ensuring quality solutions aligned with business needs.

Evaluates emerging AI technologies, platforms, infrastructure, and vendors through technical assessments, proof-of-concepts, benchmarks, and pilot deployments. Assesses solutions for capability, cost, risk, scalability, and business value; recommends adoption strategies; supports customers and internal stakeholders; delivers technical presentations, workshops, and demonstrations; and monitors developments in LLMs, automation, and agentic workflows.

Build and deploy production-grade AI products end-to-end, from problem framing and data pipelines through live deployment and iteration. Collaborate directly with city leaders and customers, lead technical scoping workshops, and develop agentic solutions involving predictive modeling, real-time decision-making, data consolidation, and LLM-powered experiences. Design scalable APIs and reliable backend and frontend systems while helping establish the technical culture of a new AI Labs team.

Build and ship production-grade AI products end-to-end, from problem framing and data pipelines through deployment and iteration. Develop agentic solutions, predictive models, real-time decision-making tools, and LLM-powered experiences. Design scalable APIs and backend systems, collaborate with product and operations teams, lead technical scoping with city leaders, and help establish AI Labs’ engineering culture.

Provide deep technical support for Devin and Windsurf by reproducing, diagnosing, and resolving complex customer issues across codebases, CI/CD, containers, cloud platforms, and developer tooling. Perform root-cause analysis, escalate with full technical context, create playbooks and tooling, and support release testing to improve reliability and adoption.

The AI Engineer will design, develop, and deploy machine learning models, work on APIs for AI applications, and manage ML pipelines on AWS. Responsibilities include productionizing AI systems and developing scalable solutions in a cloud-native environment.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Build and evolve Ridgeline's AI data platform: design secure API integrations and MCPs, implement RAG pipelines and vector database solutions, scale distributed systems, enforce data lineage/access controls, and mentor peers while shaping AI platform strategy.

Design and build scalable, cloud-native backend services and AI-powered applications. Implement agentic AI solutions and integrate LLMs with enterprise systems. Develop REST/GraphQL APIs, microservices, and event-driven architectures. Deploy on AWS, containerize with Kubernetes/Docker, build CI/CD pipelines, and ensure observability. Collaborate with cross-functional teams to deliver secure, high-performance enterprise solutions and leverage AI-assisted developer tools.

Lead and execute Generative AI strategy for Global Markets Regulatory Reporting. Define engineering approach, design scalable GenAI solutions, set technology stack, oversee cross-team design and testing, integrate GenAI tools, and drive implementation for multi-tier, microservices-based platforms to deliver business outcomes.

Lead technical design and delivery of AI-native workflows for iOS/Android mobile engineering. Build and scale agentic AI-assisted development tools, spec-driven practices, CI/CD, testing, and release automation while partnering with product, design, security, and platform teams to improve quality and velocity.

Lead enterprise AI transformation by discovering opportunities, designing and building scalable LLM/agent-based workflows, architecting data and integration solutions, partnering with cross-functional stakeholders, and driving production delivery and adoption across business units.

Design and implement scalable AI/ML-driven HealthTech microservices, contribute to technical architecture, write high-quality code, perform code reviews, develop proofs-of-concept, troubleshoot production issues, and document work while collaborating in agile teams.

Build and ship end-to-end AI-native product features across frontend, backend, and infrastructure. Integrate LLMs and agentic workflows with email, calendar, docs, and tools, design polished UX, and develop suggestion engines and workflow builders for web, desktop, and mobile. Influence product direction as an early, in-office hire.