Top AI Engineer Jobs

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.

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.

Build production-grade analytics systems that combine enterprise data, SQL, Python, dashboards, and agentic AI. Partner with leaders to analyze performance, define metrics, improve data quality, establish governance, and deliver executive insights and recommendations. Develop self-service data exploration, natural-language analytics, AI integrations, and reusable tools while collaborating with data engineering teams to resolve data issues.

As an AI Product Engineer, you'll develop AI-driven software solutions, collaborate with teams, and ensure high quality and performance of products.

Lead architecture and engineering across multiple teams and principal engineers to accelerate delivery, optimize cloud costs, create reusable foundational code and POCs, mentor engineers, resolve high-priority incidents, align technology strategy with vendors, and drive portfolio-level innovation and process improvements.

Lead technical discovery, design, prototype, and implement customer-facing GenAI solutions. Create solution architecture, security, evaluation, and operational plans; integrate with customer systems; drive production launches and reusable patterns.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Lead development of local AI agent frameworks and runtimes on Windows and GeForce RTX GPUs. Optimize agent inference, sandboxing, and privacy/security policies, collaborate with AI research and driver teams, mentor engineers, and deliver production-ready, local autonomous agent systems.

Design and build internal platforms, tooling, and AI-powered workflows to reduce developer friction, improve velocity, quality, and reliability. Partner with product, infra, and security to create reusable paved paths, measure impact with engineering metrics, and help define the Developer Experience practice.

Design, build, and deploy Tier Zero self-service solutions (virtual agents, chatbots, automated troubleshooting), intelligent routing, and predictive maintenance. Integrate automation with ITSM platforms, use low-code/pro-code tools (e.g., Power Automate), track deflection and satisfaction metrics, ensure accessibility compliance, document architectures and runbooks, and coordinate with service desk and cloud teams to drive continuous improvement.