AI Engineer – Internal Agents & Workflows

This is a remote position.

About the Project

Our Client is building custom internal AI agents and workflow automations to boost productivity across our Support, Sales, Customer Success, Recruiting, and Engineering teams. Our goal is to automate repetitive work—such as triage, summaries, follow-ups, and reporting—while enabling "copilot" experiences that help our teams execute faster with consistent quality.

This is a hands-on build role where you will integrate with our tools (Slack, Jira, Zoom, Email, Docs, CRM), implement agent workflows, and ship working automations quickly.

What You’ll Build (Key Use Cases)

You will focus on high-impact MVPs that can be deployed and improved in short iteration cycles. Examples include:

• Support & Engineering: An AI triage bot to classify bugs vs. change requests, ask required questions, and auto-create tickets with the correct fields.
  

• Sales & Customer Success: A Sales copilot that provides real-time answers grounded in product specs , and automations to generate follow-up emails, route them for approval, and send them.
  

  
  

• Recruiting: An interview evaluation agent that ingests transcripts to apply role rubrics and generate structured feedback.
  

• Internal Knowledge: Self-serve assistants and chatbots within Slack.
  

Key Responsibilities

• Agentic Workflow Design: Design and implement workflows involving tool calling, multi-step reasoning, and structured outputs.
  

• System Integration: Build robust integrations and connectors with common internal systems such as Slack, Jira, email, calendar, CRM, and data warehouses.
  

• RAG Implementation: Implement RAG (Retrieval-Augmented Generation) and knowledge systems for product and internal docs, ensuring citations and links to sources are included.
  

• Security & Access: Build basic access control patterns, including SSO/AD group-aware retrieval and role-based response filtering.
  

• Production & Guardrails: Ship MVPs fast, iterate based on user feedback, and add necessary guardrails such as evals, logging, and retry/timeout behaviors.
  

• Documentation: Document workflows and provide simple runbooks to facilitate internal adoption.
  

Required Skills & Qualifications

• Software Fundamentals: Strong software engineering fundamentals with a focus on clean, testable code and a production mindset.
  

• LLM & Agent Experience: Proven experience building with LLMs and agents, specifically regarding prompting, structured output (JSON schemas), and tool/function calling.
  

• Orchestration: Experience with multi-step orchestration (agent loops, state machines, graphs).
  

• Frameworks: Hands-on experience with at least one major framework such as Claude Agent SDK / MCP, LangGraph, or OpenAI Assistants.
  

• API Integration: Strong experience with APIs and integrations (OAuth/SSO patterns are a plus).
  

• Workflow Engines: Familiarity with workflow engines like Temporal, Dagster, Airflow, or Prefect.
  

• Retrieval Systems: Experience with retrieval systems such as LlamaIndex, LangChain RAG, Elasticsearch, pgvector, or Pinecone.
  

  
  

Tech Stack

We are flexible but expect a mix of the following:

• Languages: Python and/or TypeScript.
  

• Agent Frameworks: Claude Agent SDK and/or LangGraph.
  

• RAG: LlamaIndex or existing vector retrieval stacks.
  

• Integrations: Slack, Jira, Email, Call Recording, and CRM APIs.
  

  
  

Success Metrics (First 2–4 Weeks)

• Deliver 1–2 production-ready MVP automations (e.g., interview evaluator or support triage bot).
  

• Establish basic observability including logs, failure handling, and simple admin runbooks.
  

• Create a clear plan for scaling to more use cases with reusable connectors.