Senior AI Engineer (f/m/x)
Design, build, and operate reliable back-end services and APIs to power internal AI tools and workflows. Manage secure deployment pathways and maintain shared AI capabilities like plugins and agent orchestration.
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Design, build, and operate reliable back-end services and APIs to power internal AI tools and workflows. Manage secure deployment pathways and maintain shared AI capabilities like plugins and agent orchestration.
Evaluate the quality of interactions with AI coding agents by assessing reasoning, utility, and engineering judgment. Provide clear, opinionated written and video feedback to help define standards for AI-assisted development.
Evaluate the quality and reasoning of AI-generated coding interactions to ensure they align with high-level engineering standards. Provide clear, opinionated written and video feedback on model performance and engineering judgment.
You will build and maintain AI-native growth engines by orchestrating agent workflows and system integrations across various marketing and product channels. Additionally, you will be responsible for ensuring the reliability of these integrations and documenting workflows to enable domain specialists to tune them independently.
You will lead the development of internal tools and platforms to enhance engineering velocity, quality, and developer experience. This includes owning architectural decisions, mentoring engineers, and driving the adoption of AI-powered developer tools across the organization.
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You will lead and execute core 0-to-1 initiatives in cutting-edge domains like Space Tech. You will architect scalable solutions and drive the implementation from prototype to production.
Lead the end-to-end lifecycle of AI solutions, from discovery and design to production deployment and monitoring. Collaborate with stakeholders to define success criteria and ensure AI capabilities are effectively integrated into business workflows.
You will own features end-to-end, from design and implementation to deployment and production monitoring. You will also build and improve AI-assisted workflows and integrations while collaborating with product and operations teams.
You will lead the technical direction of AI systems and Research Flow, overseeing the end-to-end development from experimentation to production. This involves building scalable AI foundations, establishing quality standards, and collaborating across teams to deliver secure and dependable customer experiences.
You will design and build AI-powered systems for account segmentation, scoring, and sales workflow automation. You will also own the full lifecycle of these systems, including development, deployment, and performance optimization for quality and cost.
You will set the technical direction for AI at HotDocs, designing and delivering new AI-powered enterprise features from prototype to production. Additionally, you will lead AI adoption across the software development lifecycle and mentor the engineering team to improve technical excellence.
Design, develop, and maintain AI-driven solutions using Python, LLM frameworks, and modern AI tooling. Collaborate cross-functionally to build scalable, reliable generative AI and agentic systems.
You will design and build the runtime and platform for agentic systems, focusing on planning, execution loops, and tool calling. Additionally, you will build production agents that integrate with enterprise data and implement human-in-the-loop workflows to ensure reliability and traceability.
You will own the technical design and delivery of multi-component AI systems, including building evaluation frameworks and data pipelines. You will also participate in an on-call rotation to ensure system availability and iterate on model harnesses to improve performance.
Lead the design, development, and deployment of AI/ML-powered solutions while collaborating with cross-functional teams to solve complex customer problems. Oversee the end-to-end production lifecycle of AI solutions and mentor junior engineers to foster innovation.
You will own the end-to-end development of the AI cost visibility product, from backend Go services to React-based user interfaces. You are expected to work in an AI-augmented manner to design, build, and iterate on features that provide measurable impact for customers.
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You will build production AI features end-to-end, taking them from initial concept through to deployment. This involves working across the full stack, including API design, data integration, and creating streaming interfaces for document-heavy UIs.
You will design, build, and operate scalable AI systems, including generative AI applications and RAG workflows, to enhance product capabilities. Additionally, you will establish technical standards for AI development, evaluation, and deployment while collaborating across cross-functional teams.
The Lead Business AI Engineer will spearhead the development and deployment of AI-powered business solutions using Microsoft enterprise tools. This role involves leading a team of engineers, providing technical oversight for low-code solutions, and ensuring alignment with business strategy and governance.
Lead a team of AI engineers to design, build, and deploy production-grade AI services including LLMs, RAG, and agentic workflows. Own the end-to-end technical architecture, evaluation standards, and roadmap alignment with business stakeholders.
You will lead the architecture and development of production-grade AI agents and intelligence products to optimize payment processes. This involves setting technical direction for agentic systems, ensuring reliability, and mentoring engineers to deliver complex cross-team AI projects.
You will own the AI strategy, systems integration, and data architecture to support business operations and product analytics. You will also manage data pipelines, reporting, and internal AI enablement to drive efficiency across the company.
You will own the end-to-end development of AI/ML features, from research and prototyping to production deployment and monitoring. This includes designing LLM-powered solutions for feedback categorization, summarization, and semantic search while collaborating with cross-functional teams.
The QA AI Engineer is responsible for ensuring the quality, reliability, and safety of AI-driven products and proprietary LLM infrastructure. This role involves creating test plans, executing automated and manual test cases, and building evaluation frameworks to validate AI outputs and software behavior.
The role involves a 50/50 split between developing AI-driven features using Python on GCP and maintaining core platform services with Java and Spring Boot. You will be responsible for shipping LLM-backed products to production and driving AI-assisted development practices across the team.
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Design and build core agentic infrastructure including memory, orchestration, and tool-calling to automate complex operational workflows. Develop robust evaluation and observability frameworks to ensure AI systems perform reliably in a production environment.
You will build technical prototypes for AI verification and collaborate with external experts to translate research into practical policy tools. The role involves working across machine learning engineering, cybersecurity, and systems architecture to develop robust and auditable verification mechanisms.
Drive technical innovation by developing kernel generation and computational graph optimizations for next-generation GPUs. Collaborate with hardware and software teams to architect future silicon and scale AI workloads for the datacenter.
You will deliver advanced AI solutions by leveraging machine learning, generative AI, and agentic AI technologies within scalable cloud environments. Additionally, you will mentor junior developers and collaborate with customers to translate business challenges into trustworthy, responsible AI implementations.
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