Top AI Engineer Jobs

Lead end-to-end technical engagements to design, build, and deploy agentic AI and LLM-powered applications (RAG, fine-tuning, multi-agent systems). Own full ML lifecycle, backend services, integrations across cloud/on‑prem, and production LLMOps. Coordinate cross-functional teams, mentor junior engineers, and represent H2O.ai in customer workshops and executive briefings to deliver production-grade, compliant AI solutions.

Define low-level technical requirements and validate AI-enabled features for an enterprise application. Work with customers, systems engineers, developers, and stakeholders to convert requirements into Jira user stories/epics, perform functional testing, and advise on AI approaches and integrations. Engage in Agile delivery and cloud-based deployments (AWS).

Design, implement, and operate secure, scalable Azure Data and AI platform services. Lead architecture, IaC deployments, monitoring, cost optimization, and onboarding. Provide technical leadership, mentor engineers, resolve performance/reliability issues, and participate in on‑call support.

Senior ML Engineer joins a product team to design, build, deploy, and monitor production-grade AI/ML solutions. Responsibilities include algorithm and software design, pairing with teammates, creating scalable data pipelines and infrastructure, implementing model serving and monitoring, performing performance tuning and testing, and supporting product lifecycle and stakeholder collaboration.

Develop and implement AI and ML systems for Defense and Intelligence sectors, focusing on deployment, scalability, and integration of technologies.

Design, develop, and deploy production-grade AI/ML systems (NLP/LLMs, CV, generative models). Build scalable data pipelines, microservices/APIs, and cloud/edge deployments; optimize, version, and evaluate models; integrate solutions into classified operational environments and produce technical documentation.

Design, create, and implement AI and ML systems for Defense and Intelligence sectors, ensuring scalability and integration into various environments.

Design, prototype, and evaluate applied AI solutions; translate business problems into ML formulations; run rigorous experiments; build production training and inference pipelines; optimize models for production; implement dataset tooling and monitoring; ensure safety, fairness, and reliability; document findings and mentor engineers.

Optimize end-to-end AI training and inference pipelines for throughput, latency, and cost. Implement model compression, distributed training/inference optimizations, compiler-level improvements, KV cache and batching strategies, and build benchmark/regression suites. Collaborate with ML and platform teams, evaluate hardware/software offerings, and document performance playbooks.

Build, maintain, and improve production AI services for document understanding including LLM pipelines, background jobs, databases, and internal tools. Implement features, debug production issues, write tests, and contribute to evaluation frameworks to improve model quality and system reliability. Collaborate with a small engineering team and take increasing ownership over platform components.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

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.

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.

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.

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.