Top AI Engineer Jobs

The role involves enhancing kernel performance and AI-assisted tooling through developer workflows, observability infrastructure, and optimization techniques, collaborating across engineering teams.

Provide technical support and troubleshooting for complex customer issues, use scripting and OpenAI tools to automate processes, partner with engineering and product teams to resolve bugs, improve support systems, and scale operations while participating in on-call and weekend coverage.

Responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, and deployment on embedded devices, while working closely with cross-functional teams.

The Engineer III - AI designs intelligent systems, leads AI reliability efforts, collaborates on performance metrics, mentors engineers, and focuses on effective communication to solve complex issues.

As a Senior Formal Verification Engineer, you will develop verification strategies and environments for AI SoCs, ensuring the correctness of digital designs through formal verification methods and collaboration with engineering teams.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Design and implement extraction, chunking, vector indexing, and semantic retrieval pipelines for LLMs. Optimize hybrid search, reranking, and RBAC-enabled metadata filtering. Ensure HIPAA/HITRUST-compliant handling of PHI, build evaluation frameworks for retrieval groundedness, and collaborate with architects and developers to deliver context services and hand off validated prototypes.

Lead architecture and delivery of production AI agent systems: design agent infrastructure, observability, evaluation, progressive rollout, and reliability. Provide hands-on technical leadership, mentor senior engineers, drive RFCs/ADRs, and partner with product to sequence strategic platform work.

Build and operate cloud-native, large-scale data pipelines and scalable services for the Mist platform. Develop AI-driven routing intelligence to detect, troubleshoot, predict, and mitigate network issues in multi-tenant data centers, collaborating across teams and leveraging AI-assisted development tools.

The AI Marketing Ops Consultant builds AI-powered marketing automation and workflows, integrates tools, documents processes, and collaborates with teams to optimize marketing operations.

The Senior Client Engineer leads the design and management of client device solutions, focusing on MDM platforms JAMF and Intune, while mentoring teams and ensuring secure environments.

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.

Serve as Monk’s first Sales Engineer, partnering with enterprise finance customers to assess technical fit, design AR workflow solutions, build tailored proofs of concept, and implement integrations. Prototype and ship agentic AI and financial products while translating customer pain points into scalable technical solutions. The role requires deep full-stack TypeScript development, modern web infrastructure expertise, strong product judgment, customer empathy, and autonomous ownership in an in-person NYC environment.

The Computer Vision AI Engineer will develop and maintain computer vision algorithms for various systems, optimizing performance and analytics, specifically involving robots and autonomous hardware.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.

Design and build enterprise data warehouse, scalable data pipelines, integrations (OneStream, Workday, Oracle, Databricks), and AI/agent solutions (RAG, vector stores). Partner with Finance/HR/Legal/IT to translate workflows into automated, secure, maintainable enterprise data and AI services, and support analytics and reporting.

Lead architecture and hands-on implementation of large-scale Generative AI and agentic systems. Build production-ready RAG, embedding fine-tuning, vector search, and MLOps pipelines on Google Cloud. Advise clients in pre-sales, mentor engineers, and ensure reliability, latency, cost, and responsible-AI controls for high-stakes deployments.

Lead AI platform engineering for enterprise agentic and LLM systems in a regulated pharmaceutical environment. Design reusable multi-agent components, CI/CD and IaC blueprints, observability, and governance-by-design. Guide delivery teams, align deployments on approved platforms (Dataiku, Azure, AWS, Snowflake), implement evaluation frameworks and Responsible AI practices, and collaborate with Data Science, Cloud, SRE, and SecOps to ensure secure, compliant, and scalable production AI solutions.

Lead design and delivery of an enterprise Agentic AI Platform enabling build/bring-your-own-agents. Architect production-grade services for model integration, orchestration, lifecycle management, observability, evaluation, and performance optimization while partnering with cross-functional teams, enforcing governance and responsible AI, and mentoring engineers to translate experiments into scalable, secure production systems.

Design, fine-tune, and deploy LLMs and AI agents for real-time voice borrower interactions. Build evals, data pipelines, and APIs, ensure scalability and regulatory compliance, and continuously optimize agent performance for consumer lending workflows.

Design, train, and deploy AI features (speech-to-text, entity extraction, classification) for mobile and web products. Work with noisy, domain-specific data (shop-floor audio, repair notes, scanned documents), iterate models end-to-end from research to production, monitor performance (accuracy, latency, cost), and rapidly prototype new capabilities with product and engineering.

Design, develop, and maintain scalable .NET/ASP.NET Core applications on Azure, integrate AI services (Azure ML, OpenAI, Cognitive Services), build and secure RESTful APIs, implement CI/CD with Azure DevOps, use Bicep for IaC, containerize with Docker, and collaborate with cross-functional teams to deliver features.

Design, develop, and deploy AI-driven applications within SAP using SAP AI Core, GenAI Hub, and SAP Joule. Integrate models into enterprise SAP landscapes, build scalable ML pipelines, work with AWS Bedrock and SAP+Palantir for cloud-based analytics and model deployment, produce technical documentation, and recommend emerging AI practices. May mentor junior developers and collaborate with functional consultants and architects.