Job: Full Stack Software Engineer – AI-powered applications
Contract: 12+ months
Remote

What’s the role all about?

As an AI Software Engineer, you will be part of a team building AI-powered operational platforms that integrate across monitoring systems, CI/CD pipelines, ticketing tools, and cloud infrastructure. You will work on designing and implementing intelligent workflows, integrating AI models, and building scalable systems that automate complex operational tasks.

This is a highly hands-on role focused on building, integrating, and scaling AI-driven solutions in production environments.

How will you make an impact?

• Build and scale AI-driven workflows and automation systems
• Develop integrations with systems like monitoring platforms, ticketing tools (ServiceNow, Jira, OpsGenie), CI/CD pipelines, and cloud services
• Design and implement APIs, tools, and data pipelines that power AI-driven decision-making
• Work on LLM integrations, prompt engineering, and orchestration layers — streaming responses, function calling, tool use, RAG pipelines, agentic orchestration
• Build and maintain full-stack AI applications using TypeScript, React, and Next.js — from user dashboards and personalized experiences to real-time analytics and interactive tools
• Translate real-world operational problems into automated, intelligent solutions
• Collaborate with Product, SRE, and Infrastructure teams to deliver end-to-end capabilities
• Improve system performance, reliability, and observability
• Build evaluation and observability systems — measure model capabilities, monitor output quality, and create dashboards that keep the product improvable
• Create reusable platforms and tools that accelerate development — component libraries, shared abstractions, internal tooling that multiplies team productivity
   

  Key Responsibilities

• Design and develop scalable backend systems for AI-powered platforms
• Build and maintain AI integrations, workflows, and automation pipelines
• Implement REST APIs, microservices, and event-driven architectures
• Design and implement database schemas and queries for complex domains — tracking, engagement, reporting
• Work with both structured and unstructured data for AI use cases
• Contribute to CI/CD pipelines, testing, and deployment automation
• Troubleshoot and optimize production systems
• Collaborate with cross-functional teams to deliver high-quality solutions
• Contribute to reusable frameworks and engineering best practices
• Prototype fast — move from concept to working demo in days, ship incrementally

   

  What we’re looking for

• 5+ years of software engineering experience, strong focus on full-stack web development
• Expert in TypeScript and React — performance optimization, modern patterns (hooks, context, suspense), component architecture
• Production experience with Next.js — App Router, Server Components, API routes, SSR/SSG, edge deployment
• Hands-on experience with LLMs — prompt engineering, streaming APIs, function calling, tool-use, chaining and orchestration patterns
• Experience with Vercel AI SDK — unified LLM provider interface, streaming, structured output, tool calling across OpenAI/Anthropic/Google/xAI
• Model Context Protocol (MCP) — building or consuming MCP servers for extensible AI tool use
• Strong backend fundamentals — Node.js or Python, REST/GraphQL APIs, relational databases, Redis, auth
• Solid database design — PostgreSQL, Drizzle ORM, schema modeling for complex domains, query optimization, migrations
• Experience building scalable, distributed systems in cloud environments (AWS / Azure)
• Working knowledge of CI/CD, Docker, Kubernetes
• Familiarity with Tailwind CSS, Radix UI and modern component-driven UI development
• High agency — you operate independently in ambiguous environments, take ownership of problems, and drive them to completion
• Strong problem-solving and analytical skills
• Ability to work in a fast-paced, evolving environment
• Communicate effectively with both technical and non-technical stakeholders

   

  Nice to have

• Experience building agentic coding tools, AI agent frameworks, or developer-facing SDKs/APIs (Claude Agent SDK, OpenAI Agents SDK)
• Experience with Vercel ecosystem — Next.js, AI SDK providers, Turbopack
• Background in evaluation frameworks — measuring model capabilities, collecting human feedback at scale, A/B testing outputs
• Experience with sandboxed execution environments for safely running AI-generated code
• Built research tools, experimentation platforms, or scientific software
• Proficiency with Python — FastAPI/Django, data pipelines, ML tooling
• Knowledge of observability tools (Grafana, Prometheus, Sentry, etc.)
• Experience building automation or internal platforms
• Familiarity with real-time features — WebSockets, streaming UX, collaborative interfaces
• Knowledge of advanced web technologies — WebGL, WebAssembly, web workers, PWAs
• Experience with alternate JS runtimes — Bun, Deno
• Built open-source tools or platforms with active user communities
• Strong quantitative foundation (math, physics, or related fields)

   

  Representative Projects

  Things you might build in this role:

• Interfaces for collecting and managing human feedback on model outputs at scale
• Experiment orchestration platforms — launch, monitor, and analyze complex AI research runs
• Visualization tools that help understand model behavior and identify failure modes
• Reusable components and frameworks that enable rapid development of new AI applications
• Sandboxed execution environments for safely running AI-generated code
• AI-powered personalization engines — tutoring, content generation, adaptive features
• Workflow builders that let non-engineers orchestrate AI capabilities visually
• Enterprise integrations — ServiceNow, Salesforce, Confluence, Jira 

   

   

  Layer

  Technologies

  Frontend 

  TypeScript, React 19, Next.js 16 (App Router), Tailwind CSS, Radix UI

  AI SDK 

  Vercel AI SDK — unified interface for streaming, tool calling, structured output across LLM providers

  AI Integration 

  LLM APIs (OpenAI, Anthropic, Google, xAI), function calling, RAG pipelines, vector DBs, eval frameworks

  MCP 

  Model Context Protocol — build and consume MCP servers for extensible AI tool use

  Agents 

  Agentic orchestration, tool-use chaining, sandboxed code execution, headless browser agents

  Backend 

  Node.js, Python (FastAPI), REST/GraphQL APIs, Drizzle ORM

  Database 

  PostgreSQL, Redis, SQL schema design

  CLI 

  CLI tool development (Node.js/TypeScript), alternate runtimes (Bun, Deno)

  Infrastructure 

  AWS/Azure, Docker, Kubernetes, CI/CD

  Observability 

  Sentry, Grafana, custom AI eval dashboards

  Other 

  WebSockets, analytics pipelines, real-time features