At Equativ were on a mission to develop advertising technologies that empower our customers to reach their digital business goals. The impact of Generative AI on the industry is projected to be major 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 (68 people) responsible for the design development and deployment of Equativs entire GenAI stack. It is split into 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 the team youll be joining. We develop and deploy mission-critical AI agents that automate workflows for non-technical internal teams (operations ad quality marketplace finance support etc.). Our backlog targets people who today rely on manual processes and ad-hoc tooling your work will give them autonomous measurable production-grade AI assistants.
This team is part of the R&D department (200 engineers across Paris Nantes Limoges Krakow Berlin and North America) all working in an Agile environment.
What you will do
Design develop and deploy goal-oriented AI agents and full agentic systems using Google ADK and n8n automating high-impact workflows inside operational teams.
Partner directly with non-technical stakeholders sit with operations teams understand their pain points translate workflows into agentic solutions and explain your design choices in language they actually understand. This is roughly half the job.
Be the teams Python referent. You will own the production codebase enforce strong software engineering principles (unit tests CI/CD Git code review) and bring AIOps/AgentOps best practices to the team (observability versioning of agent blueprints eval pipelines).
Ship complete agentic systems end-to-end orchestration evaluation guardrails prompt management and integration with internal data sources via MCP servers that youll build yourself when needed or partner with other R&D teams when they own the system. If a partner team doesnt have bandwidth you should be comfortable picking up the integration work directly.
Detect platform-level gaps (reliability latency observability model routing) and formulate clear prioritized requests to the GenAI Core Platform team. You dont own the platform but your production usage is what pushes it to get better.
Build a working expertise in agentic design patterns (eval guardrails multi-agent orchestration) and share it with internal AI champions and AI builders as they emerge across the company.
About you
Masters 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 the teams reference on these topics.
Proven experience building production-ready agent workflows with Google ADK (strongly preferred) Langchain or LangGraph.
Practical experience with cloud platforms (Vertex AI Kubernetes or equivalents) for deploying scalable GenAI services.
Strong product/business sense you can sit down with a non-technical stakeholder draw out their workflow and come back with a working agent.
Versatile autonomous comfortable with ambiguity you turn vague business goals into concrete features without waiting for a fully specced ticket.
Fluent technical English (written and verbal) mandatory
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
IC
About the teamAt Equativ were on a mission to develop advertising technologies that empower our customers to reach their digital business goals. The impact of Generative AI on the industry is projected to be major and Equativ has been undergoing a significant transformation to embed this technology ...
About the team
At Equativ were on a mission to develop advertising technologies that empower our customers to reach their digital business goals. The impact of Generative AI on the industry is projected to be major 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 (68 people) responsible for the design development and deployment of Equativs entire GenAI stack. It is split into 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 the team youll be joining. We develop and deploy mission-critical AI agents that automate workflows for non-technical internal teams (operations ad quality marketplace finance support etc.). Our backlog targets people who today rely on manual processes and ad-hoc tooling your work will give them autonomous measurable production-grade AI assistants.
This team is part of the R&D department (200 engineers across Paris Nantes Limoges Krakow Berlin and North America) all working in an Agile environment.
What you will do
Design develop and deploy goal-oriented AI agents and full agentic systems using Google ADK and n8n automating high-impact workflows inside operational teams.
Partner directly with non-technical stakeholders sit with operations teams understand their pain points translate workflows into agentic solutions and explain your design choices in language they actually understand. This is roughly half the job.
Be the teams Python referent. You will own the production codebase enforce strong software engineering principles (unit tests CI/CD Git code review) and bring AIOps/AgentOps best practices to the team (observability versioning of agent blueprints eval pipelines).
Ship complete agentic systems end-to-end orchestration evaluation guardrails prompt management and integration with internal data sources via MCP servers that youll build yourself when needed or partner with other R&D teams when they own the system. If a partner team doesnt have bandwidth you should be comfortable picking up the integration work directly.
Detect platform-level gaps (reliability latency observability model routing) and formulate clear prioritized requests to the GenAI Core Platform team. You dont own the platform but your production usage is what pushes it to get better.
Build a working expertise in agentic design patterns (eval guardrails multi-agent orchestration) and share it with internal AI champions and AI builders as they emerge across the company.
About you
Masters 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 the teams reference on these topics.
Proven experience building production-ready agent workflows with Google ADK (strongly preferred) Langchain or LangGraph.
Practical experience with cloud platforms (Vertex AI Kubernetes or equivalents) for deploying scalable GenAI services.
Strong product/business sense you can sit down with a non-technical stakeholder draw out their workflow and come back with a working agent.
Versatile autonomous comfortable with ambiguity you turn vague business goals into concrete features without waiting for a fully specced ticket.
Fluent technical English (written and verbal) mandatory
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
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