GCP AI Jobs

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Forward Deployed Engineers serve as the technical lead for each customer deployment, making key technical decisions on architecture, data integrations, and user experience for specific customer missions. They build end-to-end AI solutions, take them into production, and support customers solving real-world physical engineering problems with novel AI capabilities. They own the technical journey from pre-sales scoping through post-deployment support and expansion, connecting AI capabilities to customer data, tools, and design processes. Responsibilities include leading discovery sessions to translate engineering challenges into AI use cases, designing and refining customer integrations, fine-tuning and evaluating AI systems in production environments, collaborating with engineering teams to develop novel AI systems, and diagnosing and resolving critical issues in live deployments.

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Build AI models and features that impact Loop's business by training, evaluating, and deploying machine learning models, particularly focusing on document extraction and understanding using multimodal large language models (LLMs). Utilize and orchestrate API LLM models to solve business problems, and handle backend engineering tasks including building atomic tasks and general servicing or automation work. Work on projects such as scaling foundation models for document extraction to multiple languages and developing AI agents for auditing freight invoices and ingesting long contracts. Collaborate with cross-functional teams and maintain high accuracy and reliability standards in AI model training and inference scaling.

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The Solutions Architecture Manager at WRITER leads and empowers a team of solutions architects, fostering their technical growth and career development across complex enterprise AI engagements. They drive the successful adoption and deployment of WRITER's generative AI platform by overseeing key pre-sales technical engagements such as use case discovery, proof-of-concept execution, and value realization for strategic customers. The role involves partnering closely with sales leadership and go-to-market teams to develop strategic account plans, define technical value propositions, and accelerate pipeline growth. Acting as an executive technical sponsor for strategic accounts, the manager builds strong relationships with C-level stakeholders and serves as a trusted advisor in AI strategy and implementation. They influence the product roadmap by gathering market insights and customer feedback, architect robust, scalable, and secure AI solutions integrating WRITER's platform with customer data and technical stacks, and transform customer evaluation and proof of concept processes to demonstrate ROI and accelerate time-to-value for clients.

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As an AI engineer at WRITER, you will architect, develop, and deploy high-performance, scalable AI applications into production environments with robust integrations to the company's end-to-end platform. You will drive the development of intelligent agents and AI-powered features by translating complex research into practical solutions for customers. Collaborating closely with research scientists, data scientists, and product managers to define technical requirements and deliver features addressing critical business needs is required. Additionally, you will contribute to the research and evaluation of emerging AI technologies, frameworks, and tools to maintain market leadership in enterprise generative AI. You will also champion responsible AI practices including bias detection, explainability, and safety alignment to ensure AI is trustworthy and ethical.

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As an AI engineer at WRITER, responsibilities include architecting, developing, and deploying high-performance, scalable AI applications into production environments with robust integrations into the end-to-end platform. The role involves driving the development of intelligent agents and AI-powered features by translating complex research into practical, impactful solutions for customers. Collaboration with research scientists, data scientists, and product managers to define technical requirements and deliver features meeting critical business needs is required. The engineer will also contribute to research and evaluation of emerging AI technologies, frameworks, and tools to maintain the company's leadership in the enterprise generative AI space. Additionally, championing responsible AI practices such as bias detection, explainability, and safety alignment to ensure AI is trustworthy and ethical is part of the role.

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Design and develop robust, scalable, and secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance, low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the platform. Collaborate closely with product managers, data scientists, and cross-functional engineering teams to translate complex business needs into innovative AI solutions, from concept to production. Implement and maintain responsive user interfaces primarily focused on backend enablement though some frontend interaction is expected using technologies like React and TypeScript to deliver intuitive user experiences. Partner with DevOps teams to build continuous deployment, logging, and monitoring systems ensuring top-tier performance and reliability. Own key architectural components, ensuring best practices in code quality, security, and maintainability through rigorous testing and peer reviews.

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Define and implement comprehensive quality assurance strategies and test plans for AI agents and LLM-powered applications to ensure product reliability and performance. Design and develop automation frameworks, creating robust, scalable, and maintainable automated test frameworks from scratch or improving existing ones using languages like Typescript, Python, or Scala. Collaborate with product managers, machine learning engineers, and data scientists to understand AI features and model behaviors, and translate them into effective test cases and validation criteria. Drive continuous improvement of testing processes and infrastructure by integrating automated checks within CI/CD pipelines to ensure rapid, high-quality releases. Identify, document, and track software defects and inconsistencies, perform root cause analysis, and provide actionable feedback to development teams. Monitor production systems and AI model performance, proactively identify potential issues, and contribute to post-release quality validation. Champion quality best practices across engineering teams to foster a culture of ownership and continuous improvement in delivering AI solutions.

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Define and implement comprehensive quality assurance strategies and test plans for AI agents and LLM-powered applications to ensure product reliability and performance. Design and develop robust, scalable, and maintainable automated test frameworks or enhance existing ones, using languages such as Typescript, Python, or Scala. Collaborate with product managers, machine learning engineers, and data scientists to understand AI features and model behaviors, translating these into test cases and validation criteria. Drive continuous improvement of testing processes and infrastructure by integrating automated checks within CI/CD pipelines to enable rapid, high-quality releases. Identify, document, and track software defects and inconsistencies, performing root cause analysis to provide actionable feedback to development teams. Monitor production systems and AI model performance to proactively identify issues and contribute to post-release quality validation. Champion quality best practices across engineering teams, fostering a culture of ownership and continuous improvement in delivering AI solutions.

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Act as a founding engineer, shaping architecture, engineering standards, and best practices. Design, build, and maintain end-to-end features across the ResolveGrid SaaS platform. Develop and operate backend services using Node.js with TypeScript/JavaScript. Build and evolve frontend application flows using modern JavaScript/TypeScript frameworks. Write and maintain Python services and supporting tools. Design and implement AI-powered features, including Chat agents and autonomous agents, Context management and memory strategies, Prompt engineering, evaluation, and iteration, and Agent workflows and orchestration. Use AI coding agents (e.g., Cursor, Windsurf, Claude) as a first-class part of daily development. Make architectural decisions with scalability, reliability, and maintainability in mind. Collaborate closely with product and leadership on roadmap and execution. Debug production issues and continuously improve system performance and reliability. Mentor and help grow the engineering team over time.

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Define scalable, top-down system architectures that unify CPU and AI technologies for next-generation automotive applications. Shape the architectural direction of the automotive and robotics portfolio to meet industry standards for performance, safety, reliability, and security. Lead technical efforts in architectural planning and execution for automotive and robotics SoCs. Collaborate cross-functionally across architecture, software, design verification (DV), and product teams to drive innovation. Communicate technical direction effectively across engineering teams and external partners. Identify future use cases and propose next-generation architectural solutions within a fast-moving, technical environment.