Top AI Engineer Jobs

As a Software Engineer, Applied AI, you'll architect and implement integrations and scalable solutions for enterprise clients, focusing on technical requirements and business goals.

Lead design, build, and scale full-stack features for the Insights practice management platform using React, Python, and PostgreSQL. Improve reliability, observability, performance, and availability. Collaborate with infra and backend teams, mentor engineers, conduct code reviews, and drive architectural improvements while delivering high-impact projects aligned with product and design.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

Lead architecture and evolution of Juniper Square’s distributed services, cloud infrastructure, and developer platform. Design scalable, multi-tenant, event-driven systems; build paved-road developer tooling, CI/CD, and observability. Hands-on coding, prototyping, and performance troubleshooting. Enable production AI/LLM workloads, ensure security/compliance, support enterprise upmarket needs, and mentor engineering teams to raise operational excellence and reliability.

As a Senior Software Product Engineer, you will enhance compliance solutions through advanced software development, performance optimization, and team collaboration to ensure high-quality outcomes and user experience.

Lead the design, development, and deployment of AI systems for investment processes, collaborating with business and technical teams to optimize workflows and ensure engineering excellence.

The AI / Machine Learning Engineer will design, build, and deploy ML models for image classification, fraud detection, and data pipelines, while collaborating with product and engineering teams.

Design and implement AI-driven applications using Verint technology, working with cross-functional teams to enhance customer experience and operational efficiency.

As a Principal Software Engineer, you will architect AI-enabled systems, mentor teams, and guide technical decisions for scalable products.

Design, build, and maintain autonomous AI-first CI/CD and QA automation pipelines for iOS and Android. Own pipeline architecture, smart test routing, flaky-test detection, device reservation and orchestration, self-healing recovery, and automated release mechanics (signing, uploads). Use AI to detect failures, predict issues, and optimize pipeline performance while maximizing device utilization and minimizing human intervention.

The Gen AI Data Engineer will design and build distributed data systems, develop data pipelines, manage data infrastructure, and integrate technologies for real-time and batch processing, contributing to scalable analytics solutions.

Build and maintain enterprise-scale ML/LLM infrastructure on Azure: create automated, secure CI/CD pipelines, provision resources with Terraform, enable real-time inference and RAG pipelines, and collaborate with data science and product teams to productionize generative AI solutions.

The Mechanical Engineer will integrate AI with manufacturing by analyzing CAD models, collaborating with data scientists, mapping manufacturing rules, and testing AI systems, ensuring accurate and efficient design-for-manufacturing workflows.

The Mechanical Engineer will apply mechanical design and manufacturing knowledge to support AI integration, perform geometric feature analysis, assist in data labeling, and coordinate cross-functional teams for DFM initiatives.

Senior individual-contributor building production agentic AI and RAG systems end-to-end: document ingestion/OCR, chunking, embeddings/vector indexes, retrieval orchestration, prompt engineering, evaluation harnesses, FastAPI services, database schemas, governance controls, and mentoring mid-level engineers.

Lead architecture and delivery of production-grade agentic AI and RAG systems. Own multi-agent orchestration, embedding/vectorization pipelines, retrieval, evaluation frameworks, and team leadership for engineers building document-intelligence and AI-assisted developer tooling. Responsible for production delivery, observability, CI/CD, and cross-team integration in an enterprise finance environment.

Embed with strategic semiconductor customers to deploy LLM- and agent-based AI into end-to-end EDA flows (RTL through signoff). Architect agentic workflows, integrate Cadence AI products, define KPIs for productivity and ROI, guide secure LLM access and infrastructure, run design reviews, and produce reusable playbooks to scale deployments across sites.

Design and implement highly available, fault-tolerant full-stack web applications and services for Starlink. Build responsive TypeScript/React front-ends, .NET microservices running in Kubernetes, monitoring/alerting, and collaborate across teams to improve architecture, observability, and user experience.

As a Senior AI Systems Performance Engineer, optimize and scale foundation models, enhance performance across various system layers, and deliver high-performance AI applications.

The Senior Infrastructure Software Engineer will architect and scale multi-cloud infrastructure, focusing on compliance, observability, and developer efficiency for enterprise AI deployments.

Design, build, test, deploy, and maintain cloud-based applications, microservices, and infrastructure. Ensure application performance and quality, fix bugs, write automated tests, and collaborate on new features. Use AI-assisted development tools daily and implement IaC and modern architecture patterns to deliver secure, maintainable code.

Senior AI Software Engineer responsible for designing, developing, and deploying generative AI, LLM-powered, and agentic AI systems. Lead system architecture, implement secure cloud-native deployments and CI/CD, collaborate cross-functionally, monitor and optimize production AI, mentor engineers, and enforce technical rigor and documentation for mission-focused federal solutions.

Develop and enhance user-facing features for an LLM observability platform using React and TypeScript. Build reusable components and front-end libraries, translate designs into performant UI code, optimize cross-device/browser performance, and collaborate with backend engineers and designers to improve usability.

Design, build, and operate secure enterprise AI platform capabilities connecting models, agents, and internal systems. Own major infrastructure workstreams (MCP gateway, enterprise context), build backend services and APIs, enable identity-aware governance (access, permissions, auditing), design agentic workflows, and take operational ownership while partnering with stakeholders to scale dependable AI tooling across Spotify.

Work with customers to discover high-value AI use cases, design and prototype LLM- and agent-based solutions, build demos and pilots, and support transition to production. Provide technical architecture, documentation, training, governance guidance, and hands-on implementation support across integrations, automation, and evaluation to enable secure, explainable, and measurable AI adoption.