Knotch logo
Knotch

Marketing technology built to drive business outcomes for Content, Demand Gen, & Growth Marketers.

AI Engineer

AI EngineerMachine Learning EngineerFull TimeRemoteSeniorTeam 51-200Since 2013Company SiteLinkedIn

Location

United States

Posted

4 days ago

Salary

$140K - $220K / year

Seniority

Senior

Bachelor Degree3 yrs expExperience acceptedEnglishPython

Job Description

• Develop and deploy AI agents that automate complex workflows across the Knotch platform. • Build the internal tools and infrastructure that power, monitor, and maintain these agents in production. • Design and implement backend and frontend services, APIs, and data pipelines, integrating AI functionality 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 across the stack. • Design and maintain a suite of 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 — guardrails, audit logging, fallback logic, and access controls that ensure our agents operate reliably and responsibly.

Job Requirements

  • You have at least 3+ years of software engineering experience building and shipping LLM-powered applications, within within SaaS startup environments (marketing or digital analytics experience is considered an asset) .
  • Prior startup, growth-stage, or SaaS platform experience working in fast-paced, agile environments.
  • Hands-on experience building production AI agents using LLM orchestration frameworks — LangGraph, LangChain, or similar.
  • Deep familiarity with agentic patterns — tool/function calling, multi-agent orchestration, memory management, and MCP.
  • Strong Python proficiency and backend API development experience.
  • Data Engineering and/or analytics background, including building pipelines, transformations, and querying data warehouses like Snowflake.
  • Solid grounding in NLP concepts — tokenization, embeddings, semantic similarity, and how language models process and generate text.
  • Experience building RAG pipelines and integrating LLMs against structured data sources.
  • Prompt engineering fluency — systematic design, structured outputs, and schema definitions.
  • An evaluation and observability mindset — you think about how to measure and monitor agent behavior, not just ship it.

Benefits

  • Comprehensive medical, dental, and vision insurance eligibility
  • 401(k) plan
  • Unlimited PTO
  • 10+ company-paid holidays
  • A daily company-wide break, and more!

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