Top AI Engineer Jobs

Build and ship production-grade agentic AI systems and LLM-powered features for financial workflows. Design RAG pipelines over sensitive data, contribute to shared AI infrastructure (LLM services, orchestration, monitoring), ensure reliability, security, and auditability, and collaborate with product and design while owning systems in production.

Build and operate production-grade agentic AI systems for cloud security and governance: design LLM-powered agents, RAG pipelines, integrate with cloud/SaaS security tools (SIEM, CNAPP/CSPM/SSPM), implement Security-as-Code/Policy-as-Code, contribute to shared AI infrastructure, and ensure secure, auditable AI operations.

Design, build, and operate production-grade agentic AI systems and LLM-powered features for financial workflows. Implement RAG pipelines over sensitive data, contribute to shared AI infrastructure (LLM services, orchestration, monitoring), and own reliability, security, and compliance in production while partnering with product and design.

Build and maintain software that integrates ML/GenAI models with Kubernetes-based edge orchestration. Develop controllers, operators, CRDs, and Helm charts to schedule and deploy AI workloads at the edge, integrate ONNX/PyTorch/TensorFlow models, use AI coding agents, contribute to open-source components, and collaborate across AI, infrastructure, and product teams to ship features end-to-end.

Hybrid technical/customer-facing role building and refining AI risk-assessment modules using LLMs, writing SQL, and creating Power BI dashboards. Lead client onboarding, translate safety programs into the platform, partner on scoping and SOWs, serve as primary technical contact, QA AI outputs, and drive product improvements from client feedback.

Lead full-stack development and architecture of a SaaS platform, design and implement AI agent integrations, mentor engineers, own system and DB design (including PostGIS), ensure CI/CD, observability, security, and scalable cloud deployments.

Build end-to-end agent-powered tools and full-stack features using Typescript/Python. Integrate LLMs, vector databases, and agentic frameworks, develop reliable backend services, work with distributed systems and cloud architectures, and ship production solutions in tight experimentation loops with cross-functional partners.

Design, build, deploy, and scale enterprise AI and Generative AI solutions (LLM apps, RAG, agentic workflows). Develop data pipelines, MLOps/LLMOps, cloud deployments (AWS/Azure/GCP), containerized services, and retrieval architectures. Partner with stakeholders, embed responsible AI, and create reusable assets and governance controls.

Lead design, development, and production deployment of complex AI systems (LLM apps, retrieval pipelines, model-driven services). Own technical direction, ensure security/privacy/responsible AI, drive MLOps and reliability, mentor engineers, and partner with product and business stakeholders to deliver scalable, governed AI solutions.

Lead design and delivery of polished frontend experiences for the CoCo coding agent platform. Define architecture and design abstractions, build scalable high-performance UI systems, collaborate cross-functionally, drive product vision and planning, and coordinate features with infrastructure and regional teams.

Lead fast-paced full‑stack development using AI coding tools (Claude, Copilot) to prompt, review, harden, and ship production-ready features. Translate requirements into prompts/specs, orchestrate multi-agent AI workflows, validate AI-generated code for correctness, security, and performance, and collaborate closely with product and design in compressed sprints.

Build and scale production-grade agentic AI applications: full-stack development, LLM and agent integration, APIs and orchestration, RAG and vector search, model lifecycle management, deployment, monitoring, and reliability with human-in-the-loop workflows and safeguards.

Design, build, and maintain production-grade AI-powered go-to-market automations (prospecting, lead scoring, campaign orchestration, attribution). Integrate ML/LLM tools into marketing and sales workflows, track business-critical metrics, ensure reliability and scalability, and collaborate with GTM strategists and non-technical stakeholders to drive measurable pipeline and revenue outcomes.

Design, develop, test, and deploy AI-powered features and tooling for the CORI robotic surgery platform using ML, generative AI, computer vision, and AI-assisted developer tools. Build integrations, services, APIs, automation, and CI/CD pipelines while ensuring cybersecurity, privacy, quality, and regulatory compliance. Collaborate on architecture, prototyping, evaluation, deployment, and continuous improvement across robotic software and cloud systems.

Lead a team to optimize algorithms for Deep Learning and Machine Learning, define technical initiatives, and collaborate with various teams for hardware and software innovation.

Build and test AI-enabled prototypes, internal tools, prompts, and agentic automations. Translate requirements into small software components, write basic unit tests, support prompt engineering and API integrations, document designs and runbooks, participate in code reviews and demos, and follow data privacy, security, and responsible AI practices while learning SDLC and team processes.

Lead development and deployment of AI/ML models and algorithms (LLMs, CNNs, transformers) across the ML pipeline. Prototype, test, document, and productionize solutions for autonomy, perception, decision support, and sensor/data fusion. Collaborate with cross-functional teams and present technical results; work with web frameworks and software engineering best practices.

Develop, test, and deploy AI/ML models and algorithms (LLMs, deep learning, embeddings, vision, sensor/data fusion). Work across the ML pipeline from data preparation and training to prototyping and production deployment. Collaborate with cross-functional teams and present technical results. Support responsible, explainable AI and participate in R&D for autonomy, detection/tracking, and decision support.

The Applied AI Software Engineer will evaluate LLM-based agents, manage benchmarking, and ensure performance, safety, and reliability in healthcare settings. Responsibilities include designing evaluation plans, running experiments, collaborating with teams, and developing internal tooling for AI features.

Design, implement, and release workflows for AI security products. Collaborate across platforms, utilizing cloud and AI technologies. Act as a technical expert, driving process improvement and supporting application development.

The job involves developing machine learning algorithms, designing data pipelines, utilizing cloud infrastructure for ML solutions, and ensuring model governance practices.

Join Ello as an AI/ML Engineer to create AI-driven educational products, enhance learning experiences, and analyze educational outcomes through machine learning.

Senior engineer responsible for designing, deploying, and operating the AI-enabled digital workplace. Own identity (Entra ID), endpoint (Intune/Jamf), and M365 services; integrate and govern AI tools (Copilot, assistants); drive IT automation, service excellence, incident/problem management, AI governance, and enablement across IT.

Design, develop, and evaluate AI safety assessment systems focused on LLMs and NLP. Investigate incident reports, conduct literature reviews, create and run NLP/LLM assessments, code or train text analytics, document methods, publish research, and collaborate with engineering teams to scale assessment tools.

Support planning, design, and implementation of a mission-critical system. Elicit and document functional and technical requirements, perform workflow/business process engineering, and act as liaison between customers and developers. Collaborate with Product Owner, Task Lead, Scrum Master, system engineers, and developers to facilitate sprint execution, functional testing, demos, and stakeholder communication.