AI Full-Stack Engineer Jobs

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Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, running in production environments across industries like finance, healthcare, and commerce. Own and drive the entire Agent Development Life Cycle (ADLC) from initial pilot through deployment and continuous iteration, building, tuning, and evolving AI agents in production environments and defining best practices for ADLC. Partner directly with leaders at large enterprises and cutting-edge startups to understand their business challenges and build AI agents that transform their operations at scale. Collaborate with customers to guide the evolution of Sierra's core platform by surfacing unmet needs, prototyping new tools and features, and working with research, product, and platform teams to shape the future of AI agent development and Sierra's product.

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You will be building innovative customer support AI products by defining AI-first product development through UI/UX, capabilities, and data models. Day-to-day responsibilities include taking ownership of challenging problems from idea to production, working closely with coding agents as a core part of your workflow, building tooling and feedback systems that enhance their effectiveness, and exercising engineering judgment to determine when outputs are ready to ship or need iteration. Senior engineers will own features end-to-end, while staff-level engineers will have opportunities to shape the technical direction of the team.

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As a Software Engineer and member of the Platform Stability team, you will help build, fine-tune, and maintain a novel AI-powered tool for diagnosing technical issues and identifying root causes. You will collaborate cross-functionally to gather requirements, develop AI/ML and analytical models, and drive data-driven insights as part of a high-performing team. Responsibilities include designing and implementing agentic AI systems with structured interfaces, reasoning loops, and robust error handling; building and maintaining data pipelines, scheduled workflows, and benchmarking infrastructure; developing evaluation and scoring systems to measure and improve model output quality; integrating the platform with internal and external services such as ticketing, messaging, storage, and observability; collaborating with cross-functional teams to translate business requirements into technical AI solutions; and architecting and maintaining production-grade AI solutions with a focus on scalability, reliability, and performance.

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As a Software Engineer, Agents, you will build systems that make AI agents indispensable to legal professionals by designing environments and actions for agentic professional work, making model selection decisions, managing context windows, creating optimal tools, and developing evaluation harnesses for faster iteration loops to unlock new capabilities. You will partner with customers and product managers to understand legal workflows, design practical evaluations to capture what excellence means, and ship agents that effectively complete tasks. Additionally, you will optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and evaluation harness development. You will work with the model infrastructure team to design and implement infrastructure for low-latency agent execution, including caching strategies, parallel tool calls, or subagent patterns. Improving observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions is also part of the role. Staying current on new developments in agentic systems and applying those insights to product development is expected.

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The Full Stack Engineer is responsible for configuring and developing "skills" for foundation models like Claude and Gemini to automate content generation, marketing flows, and business processes; developing and scaling applications within the JavaScript/TypeScript ecosystem primarily using React and integrating AI agents and automation; creating automated content generation systems for platforms such as Substack, blogs, social media, and personalized email marketing; designing and implementing sophisticated automations using platforms like Make.com or n8n to bridge data between systems such as updating records in EHR tools like Cerbo; managing PostgreSQL databases (Neon/AWS) and handling deployments within a Linux/Bash-heavy environment; helping to implement and refine voice AI solutions for call overflows and appointment scheduling including GoHighLevel integrations; managing and deploying applications on AWS (EC2) with a focus on security and HIPAA compliance; configuring and implementing AI skills across various foundation models; collaborating with AI automation experts to design scalable, efficient solutions; and establishing coding standards and best practices for AI-driven development.

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The Full Stack Engineer is responsible for building and maintaining backend services in Python, contributing to APIs and microservices with clean and maintainable abstractions, working with datasets and asynchronous processing pipelines, improving performance, reliability, and observability, participating in system design discussions and learning from senior engineers, collaborating closely with frontend and AI/ML engineers, writing production-quality, testable, and secure code, and participating in code reviews to continuously improve.

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The Product Engineer — Search at Firecrawl is responsible for owning the developer-facing search experience by shipping retrieval and ranking improvements from research as a usable product. The role involves shipping the entire product change including API updates, documentation, and examples. The engineer owns the search API end-to-end, ensuring the integration, usage, and build experience is smooth, covering response format, latency, error handling, pagination, and filtering. They use the API themselves to identify friction points before users do, closely monitor user feedback from GitHub, Discord, and support tickets, and translate research insights into product decisions in collaboration with the Search/IR and RL Research Engineers. Additionally, they run fast product experiments by hypothesizing, instrumenting, shipping, measuring, and iterating quickly to improve the search product. They are also expected to raise the bar on developer experience by fixing inconsistencies in response formats, error messages, and documentation proactively.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers.

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Lead the engineering team responsible for the Reporting product, owning the technical strategy, partnering with Product and Design to shape the roadmap, and ensuring the team has what they need to build, ship, and iterate with confidence. Build and lead a team of engineers and engineering managers by hiring exceptional people and developing them, own the technical architecture focusing on scalability, reliability, and long-term maintainability, and partner with Product, Design, and Clinical teams to translate complex customer needs into engineering solutions. Drive a culture of technical excellence by setting high standards for code quality, system design, and engineering practices while balancing shipping priorities. Serve as a key voice in company-wide engineering strategy, representing the Reporting team at the leadership level. Engage directly in technical and leadership work, setting technical standards, modeling culture, and supporting the team through challenges and high-stakes situations.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon's agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers.