Senior Software Engineer, Biggy Engineering

Responsibilities

• Build production-grade software across backend services, APIs, web applications, workflow systems, AI agents, enterprise integrations, and automation platforms.
• Architect and implement AI-native capabilities using LLMs, prompting, tool calling, agent orchestration, RAG, vector search, knowledge graphs, embeddings, and structured/unstructured enterprise data.
• Use AI coding platforms such as Cursor, Claude Code, and similar tools as a core part of your development workflow.
• Write high-quality prompts for development, debugging, product behavior, agent execution, data extraction, reasoning workflows, and customer-facing AI experiences.
• Own features from concept through delivery, including customer discovery, technical design, implementation, manual validation, release, bug resolution, and iteration.
• Work directly with customers, GTM, product, and other engineers to identify high-value problems and translate them into product capabilities.
• Make pragmatic tradeoffs between speed, quality, reliability, cost, latency, scalability, and customer impact.
• Manually validate AI and product behavior with care, especially where automated tests and evals are insufficient for probabilistic systems.
• Respond rapidly and transparently to bugs, regressions, and customer-impacting issues.
• Collaborate through paired development, design discussion, code review, debugging, and direct technical debate.

Requirements

• Strong software engineering fundamentals across backend, full-stack, distributed systems, APIs, or product engineering.
• Hands-on experience building AI-enabled product capabilities with LLMs, RAG, vector databases, embeddings, agents, workflow automation, knowledge systems, or related technologies.
• Strong practical prompting skills, including prompt iteration, context design, tool-use instructions, structured outputs, and failure-mode analysis.
• High proficiency with modern AI development tools such as Cursor, Claude Code, ChatGPT, or similar platforms.
• Ability to architect and build agent systems that can reason, retrieve context, call tools, execute actions, handle errors, and operate safely in enterprise environments.
• High agency, extreme ownership, high velocity, customer curiosity, and product judgment.
• Comfort with uncertainty, changing priorities, incomplete requirements, and fast iteration cycles.

Nice-to-Haves / Technical Areas

• TypeScript / Node.js backend services
• Fastify or similar API frameworks
• React / Next.js customer-facing applications
• Slack and Microsoft Teams applications
• Agent workflow engines and action execution systems
• RAG pipelines, vector databases, embeddings, and retrieval systems
• Knowledge graphs and enterprise knowledge modeling
• ServiceNow, ITSM, monitoring, alerting, and incident management integrations
• AWS services (S3, SQS, Lambda)
• Authentication, authorization, OAuth, SSO, API keys, and RBAC