AI Platform Engineer Jobs

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Lead and develop a team of 4-6 engineers, providing technical direction, mentorship, and career growth opportunities. Work with the engineering team to make, evaluate, and advocate for architectural decisions that align with Zapier's vision and technical standards. Collaborate across engineering teams to evaluate and inform their technical decisions, creating alignment and removing blockers for the execution of Agents roadmap. Collaborate closely with Product and Design to understand user needs—particularly around discovery, trust, and complexity—and translate those insights into intuitive, effective AI features. Champion problem-solving by identifying technical challenges and improving engineering processes. Partner with key stakeholders to tackle user problems effectively. Communicate effectively to lead your team and keep leadership informed on your team’s progress through one-on-ones, team meetings, and leadership updates. Foster a culture of experimentation, rapid learning, and continuous improvement.

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The AI Integrations Specialist is responsible for integrating Ema with a growing set of enterprise applications and SaaS tools, configuring, testing, and maintaining MCP-based integrations, APIs, webhooks, and data connectors. The role involves running and writing scripts, writing JavaScript code for workflows, managing Git workflows, and investigating integration issues. They must optimize onboarding flows for new applications and ensure seamless operation in production. The specialist supports agentic AI by ensuring Ema has the right capabilities to take actions across connected systems, building or modifying small utilities, scripts, and transformations to bridge functionality gaps, and partnering with internal teams to design integration patterns that maximize Ema's autonomy. They conduct deep evaluations on agent behavior, tool-calling accuracy, and reasoning quality by analyzing structured outputs, logs, and error cases to identify model weaknesses. They work with modeling and product teams to provide high-quality feedback that improves the EmaFusion model and design evaluation scenarios, test harnesses, and repeatable experiments. Additionally, they collaborate cross-functionally with Product, Engineering, and Customer teams to understand integration needs, help bring new customer use cases to life by expanding Ema's system capabilities, and translate customer workflows into integration requirements and actionable test plans.

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The Head of AI Platform is responsible for recruiting, retaining, and mentoring engineers and engineering managers, providing regular feedback, creating opportunities for career growth, and fostering a culture of collaboration and excellence. They act as the people and technical leader for the AI Platform team, owning staffing and execution, driving work on model serving, training compute, agent serving platform, LLM gateway, and associated orchestration layers, and guiding architectural discussions while setting strategic direction for the AI/ML infrastructure. They work closely with stakeholders to plan, execute, and support multiple projects simultaneously, managing the engineering process and platform output. The role involves owning the design, build, and operation of core AI platform components such as model serving and deployment infrastructure, compute and vendor management, MLOps pipelines and tooling, health, quality, and performance monitoring, training compute infrastructure, and LLM gateway and orchestration layers for agent serving. Additionally, the Head champions high standards in software quality, communication, collaboration, and compliance with industry and regulatory standards.

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As an AI Platform Engineer, you will design and build generative AI systems that transform large language models (LLMs) into composable, dependable tools by leveraging retrieval, tool use, agentic reasoning, and structured outputs. You will design and implement a highly reliable and scalable agent runtime including orchestration, shared state and memory, tool-calling interfaces, and scheduling to optimize cost, latency, and quality. You will build secure, sandboxed execution environments for agent actions and code, focusing on optimizing cold start, isolation, and observability. You will ship unified interfaces for various model sizes and providers and integrate with open tool ecosystems such as MCP-style connectors for data and actions. Additionally, you will develop an evaluation platform for online and offline assessments, A/B tests, safety checks, and regression gates to improve agent reliability over time. You will partner with research teams to deliver new agent capabilities from prototype to production.

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As a Senior Research Engineer, you will architect, implement, and optimize large-scale post-training systems and data processing pipelines ensuring they are reliable, scalable, and high performing. You will lead the development of next-generation LLM benchmarks and evaluation frameworks by defining standards for measuring advanced reasoning, alignment, and knowledge capabilities. You are responsible for designing and enforcing rigorous methodologies to verify data integrity and quality across highly specialized datasets. You will drive software and hardware performance optimizations to accelerate experimentation and deployment, including improvements in memory usage, training throughput, and distributed systems. Additionally, you will collaborate with cross-disciplinary teams such as research scientists, domain experts, and product engineers to validate and productionize model improvements. Mentoring junior engineers and shaping technical best practices for the post-training and evaluation engineering team is also a key responsibility. Finally, you will influence long-term research engineering strategy by identifying opportunities to systematize evaluation and data quality at scale.

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The Senior Backend/LLM Ops Engineer at Fuse Finance is responsible for driving the development and implementation of AI-powered features across the platform, specifically building and optimizing AI agent pipelines that leverage leading language models and AI services to enhance low-code platform capabilities. The role includes developing sophisticated agentic workflows to ensure high-quality AI system answers, designing and implementing AI agent systems to generate and validate use case specific workflows, user interfaces, and data schemas, and creating robust evaluation frameworks to measure and improve AI-generated outputs. The engineer collaborates with product and engineering teams to integrate AI capabilities into the existing platform architecture and establishes best practices for AI implementation, including monitoring, versioning, and continuous improvement of models.