Top AI Engineer Jobs

The AI Deployment Engineer will build, deploy, and scale AI solutions while collaborating across departments to ensure successful implementation and support of operational workflows.

Drive architecture and implementation of Databricks AI Runtime for large-scale GPU training. Build scheduling, distributed training performance, resilience, observability, and developer-facing APIs/CLI. Improve GPU utilization, fault tolerance, and multi-node orchestration. Lead cross-team initiatives, mentor engineers, and deliver high-impact platform features supporting thousands of accelerators and long-running jobs.

Lead design, implementation, and long-term strategy for cloud platforms (public/private/hybrid). Mentor teams, create best practices, drive orchestration/automation, provide Tier 3 support and root-cause analysis, and coordinate cross-team cloud initiatives.

Design, build, and scale Databricks AI Runtime for large-scale GPU training. Lead architecture and implementation of multi-node orchestration, scheduling, checkpointing, resilience, throughput optimization, observability, and developer tooling. Mentor engineers, partner with product and research, and deliver high-impact production rollouts supporting thousands of accelerators and diverse model workloads.

Design, build, and operate large-scale AI/HPC infrastructure spanning job scheduling (Beaker), runtimes, networking, and storage. Drive automation, performance optimization, root-cause analysis for distributed GPU workloads, mentor engineers, and collaborate with researchers to prioritize and deliver scalable orchestration and cluster health tooling.

Design, build, and productionize scalable AI systems for autonomous detection, RAG-backed investigations, and auto-remediation. Develop LLMs, graph-based reasoning engines, streaming pipelines, evaluation/red-team processes, and collaborate with cross-functional teams to translate attacker behavior into robust detections.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

Lead AI infrastructure projects, develop GenAI solutions, drive automation strategy, and collaborate with IT and business teams for implementation.

Design, build, and ship iOS and/or Android pet product features using AI-native workflows. Collaborate with cloud, design, and product teams, integrate event-driven data, ensure observability and quality, and contribute to CI/CD and engineering standards.

Eight-week mentor-guided weekend program that provides hands-on enterprise AI workflow and automation experience. Participants build agentic systems, RAG, API-integrated workflows, and portfolio projects using tools like n8n, OpenAI, Airtable, and PostgreSQL, with deliverables such as BRDs, workflow diagrams, and prompt libraries.

Eight-week remote weekend mentorship program providing hands-on exposure to AI workflow automation, agentic workflows, n8n, API integrations, prompt engineering, RAG systems, and capstone projects focused on enterprise automation and documentation practices.

Partner with account executives to demonstrate Arize, advise on GenAI/ML best practices, discover prospect tech stacks, lead technical demos and PoCs, handle technical objections, and drive POCs from requirements to close.

Lead design and delivery of an AI agent platform: agent orchestration, tool pipelines, memory systems, and APIs. Integrate LLMs, embeddings, vector search, and RAG into scalable, observable services. Own end-to-end designs, code reviews, cross-functional collaboration, reliability improvements, and mentor junior engineers while ensuring security and platform standards.

As a Staff Software Engineer at Cloudera, you will design and implement scalable AI and machine learning applications, collaborate with cross-functional teams, and adhere to engineering best practices.

Design, build, and operate scalable data pipelines and platforms (Snowflake, Databricks, Delta Lake). Implement dbt models, semantic layers, data quality, orchestration (Airflow/Dagster/ADF), and DevOps for data. Build AI-enabled pipelines for RAG, embeddings, vector stores and integrate LLMs into ETL. Ensure reliability, monitoring, and cost-effective cloud architectures across AWS and Azure.

Design, build, and operate scalable cloud-native backend services and platform infrastructure (Kubernetes-based). Improve reliability, performance, deployment, observability, and developer tooling, and build AI-assisted internal tools and a coding agent to enable end-to-end feature delivery. Collaborate across teams and participate in architecture, debugging, and operational ownership.

Design, prototype, and evaluate secure and robust AI/ML systems. Build models and data pipelines using modern ML frameworks, test defenses against adversarial attacks, collaborate with researchers and sponsors, and mentor junior teammates while transitioning capabilities to government customers.

Serve as the product expert and technical lead across sales cycles. Run tailored demos and POCs, handle security and integration questions, onboard customers, run and interpret admin reports, and feed field insights back to Product and Sales. Support internal training and contribute to customer retention.

As an AI Deployment Engineer, you will convert AI models into production systems, optimize performance, and work with teams to enhance user experience.

Produce and edit technical multimedia assets and AI-generated videos from LXDs/SME storyboards. Build demos, screencasts, graphics, and job aids; ensure QA, branding compliance, and seamless platform integration; maintain organized media libraries and rapidly update content to match product releases.

Architect and lead the enterprise AI platform roadmap, building scalable developer environments, multi-modal AI pipelines, agent orchestration, vector-data solutions, RAG/cognitive search capabilities, vendor ecosystems, and guardrails for model observability, drift detection, security, and responsible AI. Collaborate cross-functionally to accelerate adoption and embed AI across the enterprise.

The AI Platform Engineer will own distributed training and inference operations using Ray on GPUs, manage the lifecycle of machine learning models, optimize performance, and integrate reinforcement learning. Key responsibilities include overseeing the Ray ecosystem, configuring training and inference systems, and ensuring quality and performance metrics are met throughout the model lifecycle.

Design and implement AI-driven test automation and performance engineering for cloud-native, distributed systems. Build CI/CD-integrated automation frameworks, use ML/LLM techniques for test generation and prioritization, establish performance baselines and observability-driven validation, and mentor teams within SAFe delivery trains to embed quality across the SDLC.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.