AI Engineer [Feelance]

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 AI is projected to disrupt part of the industry, and Equativ has been undergoing a significant transformation to embed this technology at the core of its value proposition.

The GenAI team is a new, strategic unit (6–8 people) responsible for the design, development, and deployment of Equativ's entire GenAI stack. Our work is split across two complementary pillars:

• Core Platform — designing and building a reusable, industrialized Agentic Platform (MaaS, AaaS — Model/Agent as a Service) to accelerate agent development across the company.

• Business Feature — developing and deploying mission-critical AI agents that automate workflows for internal teams (operations, ad quality, marketplace, etc.).

You can join either pillar depending on profile fit, and we routinely rotate engineers between the two as priorities and product opportunities evolve. This means the bar on technical depth is consistent across both teams: we hire the engineer first, allocate the team second.

Both teams sit inside the R&D department (200+ engineers across Paris, Nantes, Limoges, Krakow, Berlin and North America), all working in an Agile environment.

Depending on the pillar you join, expect a mix of the following:

• Build production agents — design, develop, and deploy goal-oriented AI agents and multimodal/conversational experiences using frameworks like Google ADK, Langchain, LangGraph, combined with orchestration tools like n8n where appropriate.

• Own the production lifecycle of what you ship — establish robust AIOps/AgentOps practices (monitoring, versioning of agent blueprints, evaluation pipelines, reliability) within your team's scope.

• Contribute to the shared Agentic Platform (Core pillar) — gateways, evaluation frameworks, observability, MaaS/AaaS APIs — so feature teams build faster on solid foundations.

• Build agent-side integrations (Feature pillar) — develop the MCP servers and backend services that agents need to interact with enterprise systems, in partnership with other R&D teams (and picking up the work yourself when a partner team doesn't have bandwidth).

• Be the Python referent in your team — own production-quality Python, enforce strong SWE principles (unit tests, CI/CD, Git, code review), and bring AIOps/MLOps best practices wherever you sit.

• Build a working expertise on agentic design patterns (eval, guardrails, multi-agent orchestration) and share it with AI champions and AI builders across the company as the GenAI Center of Expertise takes shape.

• Engage with stakeholders — talk to internal teams to understand operational pain points and translate them into measurable GenAI solutions. You don't lead cross-team architecture, but you should be credible across the org.

• Master's degree in Computer Science, Data Science, or a similar technical field.

• 3+ years as a Python Developer or ML Engineer, with a recent focus on deploying LLM-powered solutions in production.

• Mastery of Python for enterprise-level development, strong knowledge of core software engineering principles, and hands-on experience with AIOps/MLOps (unit tests, CI/CD, Git, observability). You should be comfortable being your team's reference on these topics.

• Proven experience building production-ready agent workflows, orchestration layers, or platforms using Python frameworks (ADK, Langchain, LangGraph).

• Practical experience with modern cloud platforms (Vertex AI, Kubernetes or equivalents) for deploying scalable GenAI services.

• Strong versatility and a demonstrated willingness to work across the full stack and across functional expertises.

• 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) — mandatory.