PhantomBuster is a web automation SaaS that allows businesses to grow faster. We enable thousands of companies to boost their growth by finding and connecting with their ideal customers seamlessly.
Founded in 2016, PhantomBuster developed a toolbox of over 120 flows (Phantoms) to help businesses scale their sales and marketing processes. We allow our users to automate finding and enriching data about their potential customers and leverage that data to connect with them.
● Bordeaux office based role
● Initial 6-month engagement
We are building agentic AI into the core of our product and need someone who can help us move faster — not by learning as they go, but by bringing real, hands-on experience with agentic systems from day one.
You will work alongside our current ML Engineer to set standards, build the frameworks others will follow, and advise engineering teams across the company on how to implement AI agents the right way. This is a greenfield opportunity: you will shape how we do things, not inherit someone else's playbook.
You will join our Data Department to support the development of Phantom Intelligence, the platform that powers our product's AI capabilities.
The Data Department currently consists of two teams:
Analytics Team: composed of three Analytics Engineers
Machine Learning Team: composed of one Machine Learning Engineer.
Our AI stack runs on AWS Bedrock AgentCore, with agents built in Python 3.13 / LangChain / LangGraph, observed via Langfuse, and tested with an in-house evaluation framework. The data platform combines an operational PostgreSQL database, an AWS data lake, and a Snowflake data warehouse. Data and reporting are also available through self-service tools such as Amplitude, Tableau, Google Analytics, and ChartMogul.
Define and evolve our infrastructure to allow for better ML and AI capabilities, with a focus on LLM-based and agentic systems.
Contribute to the development and expansion of our agentic AI framework powered by AWS Bedrock, enabling both internal tools and customer-facing features.
Identify, source, and refine datasets to allow tuning models, powering retrieval pipelines, or expanding agentic workflows.
Pre-process data by using techniques such as data cleaning, feature engineering, and transformation.
Train, evaluate, and deploy both LLM-based systems and traditional machine learning models into production.
Monitor, debug, and continuously improve deployed models and AI tools.
Support machine learning usage throughout the company, including selecting the right modeling approach for the use case (LLM vs. traditional ML).
Support the integration and use of LLMs, including approaches such as fine-tuning, prompt tuning, and retrieval-augmented generation (RAG), to improve accuracy.
You have an analytical mindset.
You strive to understand business challenges and leverage ML and LLMs to solve them.
You have genuine curiosityabout agentic AI — you've explored it because it excites you, not just because it's trending.
You are autonomous and rigorous. You have an ownership mindset you define the path, challenge assumptions, and aren't afraid to break new ground.
You can explain complex AI concepts clearly— to engineers, to non-technical stakeholders, to anyone. If you can't articulate how something works, that's a signal.
You're brave enough to try thingsin a space where best practices are still being written.
You're resourceful - you might not have all the answers, but you are ready to find them.
You are a team player with high integrity - you can remain flexible as we grow.
5+ years of experience as an ML Engineer, AI Engineer, or Software Engineer with a strong AI focus.
Hands-on experience building AI agentsusing frameworks such as LangChain, Amazon Bedrock AgentCore, or similar.
Strong understanding of LLM-based systems: prompt engineering, agent orchestration, tool use, and multi-agent workflows.
Familiarity with MCP (Model Context Protocol)and experience integrating agents with external APIs or data sources.
Experience working with Agents for Amazon Bedrock AgentCore or similar agent setups.
Strong understanding of machine learning algorithms, statistical methods, and data preprocessing techniques.
Experience with cloud platforms for model training and deployment, especially AWS.
Proficiency in Python, including LangChain, and standard data libraries (Pandas, NumPy, etc.).
Fluency in English.
Direct experience with conversational chat agents/sub-agents (LangChain/LangGraph, Pydantic structured outputs, tool calling) and shared evaluation infrastructure (DeepEval, Langfuse traces, cross-agent I/O contracts).
Background in MLOps: model monitoring, CI/CD pipelines, versioning (MLflow, Airflow).
Contributions to open-source ML or AI projects.
Experience in a SaaS B2B or product-led growth company.
The easiest way to find and engage with warm leads.
Leverage unique and live profile data and intent signals to
- Find qualified leads at scale
- Enrich with real-time data
- Scale personalized outreach.
