Top AI Engineer Jobs

Design, develop, and deploy scalable Generative AI and agentic AI systems. Build and orchestrate LLM-based agents, architect end-to-end AI pipelines, implement MLOps and CI/CD, deploy on cloud platforms with containerization, and ensure monitoring, observability, security, and governed data access using Databricks and MLflow.

Lead and manage an AI engineering team to design, develop, and implement machine learning and deep learning models. Collaborate with cross-functional teams to integrate AI solutions, provide technical leadership, and stay current with AI advancements to drive continuous improvement.

Design, develop, and deploy advanced ML solutions for smart buildings and industrial automation. Integrate models into BMS/ICS (SCADA/PLC), build real-time inference pipelines, optimize performance across cloud and edge (AKS/GKE/microk8s), collaborate with data engineering and MLOps, lead POCs, mentor junior engineers, and ensure security and industrial compliance.

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.

Design, implement, and operate LLM-powered and agentic AI features and shared infrastructure. Build RAG pipelines over financial data, integrate LLMs and tools into agentic workflows, and support production reliability, monitoring, and evaluation. Collaborate with product and design to translate customer needs into safe, compliant AI solutions.

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 deploy production-ready generative AI solutions (RAG, agentic workflows, MCP). Mentor engineers, partner across teams, create data pipelines and feature engineering, instrument observability, and optimize LLM performance to improve system reliability and incident response.

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.

The Senior AI/ML Engineer will enhance developer productivity by improving Python workflows, leading projects, mentoring engineers, and driving best practices.

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, deployment, and operation of production ML systems and inference pipelines. Build and scale APIs and services on Kubernetes, optimize pipelines, monitor model performance and drift, and partner with cross-functional teams to deliver ML insights to customers.

Design and implement AI/ML systems for healthcare, own inference and delivery, manage/scale production ML systems, build scalable data pipelines and APIs, apply MLOps practices (deployment, monitoring, drift detection), and work with large-scale data and distributed computing frameworks.

The Senior AI Product Engineer will develop AI features integrated into products, focusing on user experience, full-stack development, and collaboration with AI engineers, product managers, and design teams.

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.

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.

Design, build, and deploy generative AI/LLM solutions on GCP/Vertex AI. Integrate large structured and unstructured data, implement RAG and prompt engineering, embed GenAI into pipelines, APIs, and dashboards, and communicate technical results to stakeholders.

Design, build, and deploy generative AI/LLM solutions on GCP/Vertex AI. Integrate diverse data sources, implement RAG and prompt engineering, embed AI into pipelines, APIs, and dashboards, and communicate technical results to technical and non-technical stakeholders.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

Own the agent-facing integration layer (MCP server) that exposes multimodal video/image AI capabilities to developers and agents. Build auth (OAuth/OIDC/RBAC), multi-tenant isolation, usage tracking, rate limiting, and reliability for enterprise scale. Design API contracts, handle failure modes, and partner with research to productize video understanding features.

Design, develop, integrate, deploy, and maintain AI solutions across sales, manufacturing, finance, logistics, and IT. Collaborate with cross-functional teams, document systems, provide user training and support, research emerging AI technologies, and troubleshoot production deployments.

The AI Engineer develops and deploys machine learning models for manufacturing, builds data pipelines, and implements MLOps practices while collaborating with stakeholders to translate business challenges into effective AI solutions.

The Principal AI Engineer will design and deploy AI-driven solutions, integrate AI into data processing pipelines, and drive market insights using advanced AI techniques. Responsibilities include building scalable infrastructure, developing LLM solutions, and partnering with multi-disciplinary teams.

Interns build and test LLM-based agents, research agent frameworks and tooling, prototype integrations with internal APIs and M365, write code and documentation, participate in reviews, and present findings to AI Lab leadership.

Build and ship LLM-powered, agentic AI solutions within cross-functional squads: implement RAG, retrieval pipelines, tool use and agent workflows; integrate AI into enterprise systems; ensure observability, guardrails, reuseable patterns, and measurable outcomes; support handover and stakeholder alignment.

Lead an embedded AI squad from discovery to production: harden and deliver AI prototypes, design reusable agentic AI patterns and architectures, ensure security/data governance, integrate AI into enterprise systems, codify reference implementations, mentor engineers, and measure outcomes to scale solutions across the organization.