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AI Engineer

Job in Jacksonville, Duval County, Florida, 32290, USA
Listing for: Knotch
Full Time position
Listed on 2026-07-08
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Backend Developer
Salary/Wage Range or Industry Benchmark: 140000 - 220000 USD Yearly USD 140000.00 220000.00 YEAR
Job Description & How to Apply Below

We're a growth-stage technology company helping brands optimize their content performance and harness AI for marketing success. With a fast-paced, agile, entrepreneurial culture, we move quickly, adapt constantly, and thrive in dynamic environments. Through our Knotch platform, we meet our clients where they are and enable them to prove and improve their content. With our newest innovation, AgentC—a managed AI service for marketers—we're transforming how marketing teams plan, adapt, and execute across digital channels.

About

the Role

Most companies building AI agents are starting from scratch. Knotch isn't. We've spent years accumulating one of the richest proprietary datasets in B2B content intelligence — deep signals on how content performs, what drives engagement, and what separates great content from noise. That data is our unfair advantage, and we're now ready to unleash it.

We're at the beginning of an exponential shift — moving from a platform that surfaces insights to one that autonomously acts on them through Gen AI and Agentic AI. As a Sr. AI Engineer, you'll have access to a unique data foundation and a leadership team fully committed to making AI central to everything Knotch does. This is a rare chance to build consequential AI — not demos, not experiments, but production‑grade agents that enterprise clients depend on.

This isn't an AI layer bolted onto an existing product. This is a ground‑up reimagining of what content intelligence can be in the age of large language models and agentic AI. You'll shape not just how we build, but what we build and why. If you want to own the architecture, influence the roadmap, and watch your work directly move the needle for enterprise clients — this is that role.

Responsibilities
  • 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.
Qualifications

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) .

Must Haves
  • 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 — Lang Graph, Lang Chain, 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.
Nice‑to‑Haves (not mandatory)
  • Experience with vector databases (Pinecone, pgvector, Weaviate) or data warehouses like Snowflake.
  • Familiarity with AI guardrail frameworks — Guardrails AI, NeMo Guardrails, or Llama Guard.
  • Exposure to…
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