Generative AI Developer

About the team

At Equativ, we're on a mission to develop advertising technologies that empower our customers to reach their digital business goals. The impact of Generative Artificial Intelligence on advertisers is projected to disrupt part of the industry and Equativ has been undergoing a significant transformation to embed this technology at the core of our value proposition.

The GenAI team is a new, strategic unit (4−5 people) responsible for the design, development, and deployment of our entire GenAI stack. Our mission is to create intelligent, autonomous AI agents that automate complex tasks and drive direct business value for our key product lines. Our work is split into two pillars:

- Core Platform: Designing and building a reusable, industrialized Agentic Platform (MaaS, AaaS – Model/Agent as a Service) to accelerate the development and deployment of agents across the entire company.

- Business Feature: Developing and deploying the first set of mission-critical AI agents directly integrated into a major product, in close partnership with a key Business Line.

This team is part of the R&D department which is composed of 200+ engineers spread across Paris, Nantes, Limoges, Krakow, Berlin and North America all working in an Agile environment and ready to tackle the most complex technical challenges.

The Role

We are looking for a passionate, results-oriented Senior Python/GenAI Agent Developer with a strong entrepreneurial spirit. You will be a core technical pillar of the team, playing a central role in building both the foundations and the initial features of our GenAI platform. Your versatility will be essential for navigating development tasks across the backend, core platform, and data engineering layers.

•  Product-Focused AI Agent Development
• Design, develop, and deploy goal-oriented AI agents and multimodal or conversational experiences (leveraging frameworks like Langchain, LangGraph, or AutoGen) to automate complex, high-impact workflows within our flagship product
• Own the industrialization and production lifecycle of deployed agents, establishing robust AIOps/AgentOps processes for monitoring performance, ensuring version control of agent blueprints, and guaranteeing production-grade reliability and low-latency response times
• Collaborate closely with Product Managers to translate complex business challenges into concrete, measurable GenAI solutions, focusing on maximizing the return on investment (ROI) of LLM and agent usage
• Develop the specialized backend services and tool-calling APIs necessary for agents to interact securely and effectively with our existing enterprise systems and data sources

• Core Agentic Platform Construction
• Develop modular, reusable components for the internal Agentic Platform, including dynamic toolkits, sophisticated orchestration layers, and specialized evaluation harnesses for agent performance and safety
• Implement and optimize the entire data infrastructure pipeline critical for GenAI, including managing Vector Databases, designing highly efficient RAG (Retrieval-Augmented Generation) pipelines, and establishing mechanisms for continuous Fine-Tuning 
• Champion and implement GenAI-native development standards, integrating best practices for AIOps, CI/CD, and documentation to ensure maximum code reusability and operational quality at scale
• Lead the technical watch on the evolving LLM landscape (e.g., open-source vs. proprietary models) and spearhead the adoption of advanced techniques such as prompt engineering, multi-agent coordination, and complex agentic pattern design

• Master's degree in Computer Science, Data Science, or a similar technical field
• 3+ years of significant experience as a Python Developer or ML Engineer, with a recent focus on deploying solutions powered by Large Language Models (LLMs)
• Proven expertise in building production-ready AI agent workflows, orchestration layers, or platforms using Python frameworks (e.g., Langchain, LangGraph, or AutoGen)
• Mastery of Python for enterprise-level development, strong knowledge of core software development principles, and experience integrating AIOps/MLOps best practices (unit tests, CI/CD, Git)
• Practical experience with modern cloud platform technologies (VertexAI, Kubernetes or equivalents) for deploying scalable GenAI services
• Strong versatility and a demonstrated willingness to work across the full stack—from backend agent logic to Vector DB and RAG infrastructure
• Entrepreneurial mindset, high autonomy, and the ability to turn ambiguous, high-level business goals into concrete, efficient GenAI features
• Fluent technical English (written and verbal).