AI Agent Builder

Mirakl•Boston, MA

2d•Hybrid

About The Position

Mirakl is seeking an AI Agent Builder to design, build, and scale internal AI agents that integrate with company systems, automate workflows, and deliver measurable operational impact. This role is crucial for industrializing AI agents from experimentation to production, leveraging Mirakl's position as an operating system for intelligent commerce. The successful candidate will be at the center of developing the company's AI-driven future, working with existing AI deployments and shaping what comes next.

Requirements

5 plus years of experience as a Data Scientist, ML Engineer, or Software Engineer.
Background in backend/full-stack development, data, or automation workflows.
Proven experience building AI agents using LLMs, RAG, MCP and related technologies, including architectural design of multi-step agentic workflows.
Proficient in Python and data fundamentals (SQL, JSON, data modeling).
Production deployment expertise; have shipped AI solutions in production.
Experience with API integrations, MCP, webhooks, and connectors.
Experience with low-code/no-code automation platforms (n8n, Make, Zapier or equivalent).
Curiosity and continuous learning; stay current with AI/ML trends and love exploring new technologies.
Strong communication skills in both English and French.
Solid on APIs, reliability, and scalable integrations.
Growing experience in LLMs.
Strong on data structuring and business use cases, relevant for analytics-driven agents.
Good understanding of LLMs, RAG, and evaluation, with potential to grow on productization.

Nice To Haves

Backend Engineer with AI exposure
Data / Analytics Engineer
ML / Applied AI Engineer

Responsibilities

Design and build workflow-driven AI agents (e.g., BDR, Contract, Accounting agents).
Architect multi-step agent systems including prompts, tools, memory, and guardrails.
Rapidly prototype solutions and evolve them into production-grade products.
Connect agents to internal systems (CRM, data platforms, Drive, Jira, etc.).
Enable agents to perform real actions (e.g., update CRM, generate reports, create tickets).
Ensure reliable orchestration between APIs, tools, and data sources.
Run pilots with end users and gather qualitative and quantitative feedback.
Analyze usage, errors, and performance metrics.
Continuously improve agent accuracy, reliability, and user experience.
Implement robust guardrails, validation layers, and access controls.
Ensure compliance with data security and governance standards.
Clearly document each agent (scope, capabilities, limitations, KPIs, roadmap).
Build a clean, scalable agent ecosystem for internal users.
Support and educate teams on adopting agents in their workflows.
Contribute to internal best practices and agent-building standards.
Act as a champion for AI-driven ways of working.
Collaborate with process owners, the Solution Architect, and the Data team.