Top AI Engineer Jobs

The role involves building AI agents for healthcare practices, focusing on reliability, data pipelines, automation, and production systems.

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.

The role involves building and maintaining AI infrastructure, optimizing training performance, and developing tools for robot training and data workflows.

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.

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.

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.

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.

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.

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

Lead Responsible AI projects to detect, mitigate, and evaluate bias across GenAI, foundation models, and recommender systems. Collaborate with product and platform teams, deploy fairness interventions at scale, mentor junior engineers, and define technical strategy to align models with Pinterest policies and business metrics.

Design, develop, and maintain scalable AI/ML systems and end-to-end pipelines (data preprocessing, model training, prompt engineering). Collaborate cross-functionally, optimize model performance, ensure fairness and security, monitor production systems, troubleshoot issues, mentor junior engineers, and recommend improvements to data infrastructure and tooling.

Lead technical design and execution for an AI/ML mission platform: drive architecture decisions, lead major engineering epics, simplify distributed systems, optimize edge and cloud performance, mentor engineers, integrate ML models and inference pipelines, and represent technical capabilities to DoD stakeholders.

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.

The Principal Engineer will lead the design and development of technical solutions, oversee project teams, and maintain existing solutions, focusing on APIs and innovative technology applications.

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

Develop, analyze, and implement application systems using Python and generative AI frameworks. Build and maintain RESTful APIs, cloud-deployed containerized services, and ensure security, testing, and operational support. Consult with stakeholders, perform feasibility and post-implementation analysis, mentor junior analysts, and drive compliance with policies and risk controls.

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, 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.

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 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.

Design, build, and maintain generative AI and ML solutions (LLMs, RAG) on AWS. Develop document-processing bots (OCR/ICR/OMR), APIs, scalable model deployments, monitoring, and continuous improvement. Collaborate cross-functionally to embed AI capabilities and follow DevSecOps and Agile practices.

Lead design and implement a scalable, secure multi-tenant SaaS using .NET and Angular. Own end-to-end feature delivery, leverage AI-assisted coding, apply DDD/CQRS/event-driven patterns, optimize data and SQL performance, mentor engineers, and maintain CI/CD, observability, and automated testing.

Build and maintain landing pages, product pages, and blog templates using HTML/CSS/JavaScript within the CMS. Improve site performance, SEO, and conversions; implement forms and analytics; write and publish web copy and long-form content using AI tools; build lightweight automations and report on site and campaign performance.

Design, build, and deploy enterprise AI solutions and agentic systems integrating LLMs with company data and workflows. Develop RAG applications, conversational agents, and Copilot/Claude-based agents; productionize systems with CI/CD, monitoring, and vector DB retrieval. Collaborate with stakeholders, measure impact, and mentor teammates.

Design next-gen semiconductors for AI/ML applications, leading advanced development processes and optimizing hardware-software integration.