Applied AI Engineer Jobs

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The Partner AI Deployment Engineer leads technical delivery with OpenAI partners across EMEA, supporting the design, deployment, and scaling of production-grade AI solutions across multiple industries and use cases. They act as the primary technical delivery partner for OpenAI partners, working with partner delivery teams and customer stakeholders to translate solution designs into deployable, production-ready architectures on the OpenAI platform. The role includes supporting customer time to value through hands-on prototyping, integration support, architectural guidance, and troubleshooting during critical phases of delivery. The engineer collaborates closely with Solutions Engineers, Forward Deployed Engineers, and other ADEs to engage the right technical expertise from design through production rollout. They help partners operationalize solutions by addressing scalability, reliability, security, and safety considerations for enterprise production environments and contribute to reusable deployment patterns, reference architectures, and delivery guidance for repeatable execution. The engineer acts as a technical quality and governance point during deployments to ensure solutions meet OpenAI’s standards before and after go-live and captures feedback from deployments to share insights for improving delivery playbooks and platform capabilities.

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Design and develop AI applications primarily in Python, run evaluations to validate models, and package solutions for Kubernetes, AWS, or adapt them to customer on-premises clusters. Lead discovery sessions with customers, guide pilot projects, and ensure successful deployments, collaborating mostly remotely with occasional on-site workshops. Monitor system performance and reliability, add to logging, billing, and auth services, and build internal tooling to automate repetitive tasks. Provide feedback on patterns, pain points, and reusable modules to the core product team to influence the future direction of the AI platform.

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The role involves building and taking ownership of an AI-native automated testing platform used by customers at every stage of the Software Development Life Cycle (SDLC). Responsibilities include solving hard problems that involve product and technical ambiguity, coding efficiently, managing one's own work and projects autonomously, and focusing on building and shipping high-quality code rather than meetings. The engineer will integrate large language models (LLMs) into real-world applications, including performance tuning, prompt engineering, context management strategies, and conducting evaluations and observability of LLMs.

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Build and maintain autonomous agent-based solutions that generate real impact on internal teams to better serve customers. Design, architect, and deliver AI solutions by partnering with technical and business teams. Own the end-to-end lifecycle from design through experimentation, deployment, user adoption, impact measurement, and continuous iteration. Mentor and scale expertise by coaching engineers, setting direction for best practices, and acting as a technical sounding board. Collaborate cross-functionally to ensure solutions meet user needs, generate company-wide impact, and align with other Engineering squads.

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Partner with customers to build and deploy Generative AI and machine learning solutions from use case scoping and data exploration to model development and deployment, leveraging Snorkel Flow or designing custom approaches. Develop and implement state-of-the-art AI systems including retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Create augmented real-world datasets and evaluation workflows to ensure model reliability and transparency. Manage relationships with customers' leadership and stakeholders to ensure successful AI project development and deployment. Collaborate with pre-sales Solutions and Product teams to map customer needs to capabilities and prioritize roadmap gaps. Work with other Applied AI Engineers to standardize solutions and contribute to tooling and best practices. Lead stakeholder education on AI quantitative capabilities and serve as the voice of customers for AI paradigms and workflows. Conduct enablement workshops for knowledge transfer to customers using Snorkel AI. Annual travel up to 25%.

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The QA Engineer, AI is responsible for owning and expanding the end-to-end automated test suite using frameworks like Playwright, Jest, and Vitest across all deployment surfaces including web app, iPad, Epic, Cerner, Surgery Connect, EMIS, and multiple browsers. They design test cases covering functional requirements, edge cases, and failure modes, integrate tests into CI pipelines to gate every PR, and use AI coding agents to accelerate test creation and maintenance. They build end-to-end simulation suites for clinical AI pipeline evaluation, create smoke tests, collaborate with the ML team on AI output evaluations, and detect quality regressions. The role involves managing the QA process for versioned releases, running regression and manual exploratory tests, managing release checklists, maintaining requirements traceability matrices linking software requirements to test cases and results, supporting audits with documentation, automating compliance workflows, logging and triaging defects, working with developers to reproduce issues and verify fixes, and ensuring no high-severity defects ship without resolution and re-testing.

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Partner with customers to build and deploy impactful Gen AI and machine learning solutions, including use case scoping, data exploration, model development, and deployment. Develop and implement state-of-the-art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. Manage relationships with customers' leadership and stakeholders to ensure successful AI project development and deployment with Snorkel Flow. Collaborate with pre-sales Solutions and Product teams to map customer needs to capabilities, prioritize roadmap gaps, and guide project setup. Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices. Lead stakeholder education on quantitative capabilities and help them understand different AI approaches. Serve as the voice of customers for new AI paradigms, data science workflows, and provide customer feedback to product teams. Conduct enablement workshops to transfer knowledge to customers using Snorkel AI. Annual travel up to 25%.

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Design multi-agent systems with Subagents/Handoffs/Router patterns, implement agent logic using langchain/langgraph, design comprehensive evaluation frameworks, optimize prompts with A/B testing, implement state management (short-term and long-term memory), and design RAG patterns with vector store integration. Guide customers on agent deployment and configuration management, integrate agents into CI/CD pipelines, collaborate with Solution Architects on infrastructure requirements, and set up observability using LangSmith. Lead agent engineering maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Solution Architects, Engagement Managers, and Product/Engineering teams.

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Set up end-to-end evaluations to measure and improve agent performance. Experiment with new agentic techniques such as multi-agent systems and reasoning-from-feedback (RFT). Build lightweight tools, servers, and orchestration layers like MCP servers that enable agents to operate reliably in production. Stay on top of emerging research and blogs on LLM/AI agents and bring ideas into production experiments.

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The Audio Engineer will own and scale audio quality across voice AI products, ensuring voices sound great to human listeners across thousands of voices and recording conditions. Responsibilities include identifying and correcting audio artifacts, loudness inconsistencies, frequency imbalances, and sibilance issues in large-scale voice datasets; designing and implementing scalable audio processing pipelines including EQ, compression, de-essing, dynamic range optimization, and normalization strategies; optimizing audio quality across real and synthetic voices for consistent product experience; leading audio quality decisions during on-site voice actor recording sessions such as microphone selection, placement, gain staging, and environment setup; defining, documenting, and enforcing audio quality standards for external vendors to meet training and product needs; converting manual audio workflows into automated, repeatable, code-based systems; collaborating with research to improve training data quality, especially TTS speaker-specific fine-tuning; and contributing to synthetic data pipelines by defining and validating acoustic characteristics and guiding sound profile production and evaluation.