RRemoteville

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Knotch logoKnotch

AI Engineer

Skills
Software As A ServiceCritical ThinkingData WarehousingDatabasesEasily AdaptablePlatform DesignSnowflake Cloud
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • 3+ years of software engineering experience building and shipping LLM-powered applications in SaaS startup environments
  • Prior startup growth-stage or SaaS platform experience in fast-paced agile environments
  • Hands-on experience building production AI agents using LLM orchestration frameworks such as LangGraph or LangChain
  • Deep familiarity with agentic patterns including tool or function calling multi-agent orchestration memory management and MCP
  • Strong Python proficiency and backend API development experience
  • Data engineering and analytics background including building pipelines transformations and querying data warehouses like Snowflake
  • Solid grounding in NLP concepts including tokenization embeddings semantic similarity and language model behavior
  • Experience building RAG pipelines and integrating LLMs against structured data sources
  • Prompt engineering fluency with structured outputs and schema definitions
  • Evaluation and observability mindset for measuring and monitoring agent behavior

Nice to have

  • Adaptable
  • Curious
  • Ownership mindset
  • Initiative
  • Fast-paced
  • Collaborative
  • Strategic thinker

Day to day

  • Develop and deploy AI agents that automate complex workflows across the Knotch platform
  • Build internal tools and infrastructure to power monitor and maintain these agents in production
  • Design backend and frontend services APIs and data pipelines integrating AI into user-facing features
  • Contribute to system architecture and core platform design alongside the broader engineering team
  • Collaborate closely with Product Data Engineering Backend and Frontend teams to align AI capabilities with product direction and ensure seamless integration
  • Design and maintain evaluations and benchmarks to measure agent accuracy reliability and cost-effectiveness
  • Optimize inference pipelines and backend systems for speed scalability and cost
  • Own the AI safety and governance layer including guardrails audit logging fallback logic and access controls

Hiring process

  • Input code AI-ENG-2026 with application