Founding AI Platform Engineer (MLOps / Backend)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Founding AI Platform Engineer (MLOps / Backend) based in France.

This is a foundational engineering opportunity for someone who wants to shape the systems that turn ML and GenAI capabilities into reliable, production-ready products.

You will operate at the intersection of backend engineering, cloud infrastructure, MLOps, platform reliability, and product delivery.

The role offers broad ownership across model lifecycle management, deployment, serving, experimentation, observability, and CI/CD.

You will build the production infrastructure that enables AI systems to scale safely while remaining performant, maintainable, secure, and cost-efficient.

Working closely with ML and product teams, you will turn ambiguous technical challenges into practical, durable solutions.

As an early platform engineer, your decisions will directly influence engineering standards, tooling, reliability practices, and the future scalability of the platform.

Accountabilities

• Build and maintain infrastructure and tooling for training, evaluating, deploying, serving, and monitoring ML models and GenAI services.
• Develop and operate production backend services, APIs, and pipelines supporting recommendations, agent workflows, and customer-facing integrations.
• Improve CI/CD pipelines, automated testing, release processes, rollback strategies, and environment management.
• Establish comprehensive observability across application health, model behavior, agent quality, latency, costs, and operational failure modes.
• Build reproducibility and lifecycle management practices for models, prompts, datasets, configurations, and software releases.
• Support experimentation and measurement infrastructure that enables ML and product teams to evaluate changes reliably.
• Strengthen platform reliability, scalability, security, performance, and cost efficiency across the technology stack.
• Troubleshoot production issues end-to-end and convert recurring operational problems into long-term engineering improvements.
• Collaborate closely with ML, product, and engineering teams to move ambiguous initiatives from concept to completion.
• Establish engineering standards and platform practices that can support future growth and increasing system complexity.
• Identify platform, reliability, and scaling risks early and proactively address them before they affect customers or delivery.

Requirements

• Strong software engineering background with experience building, deploying, and operating production systems.
• Proven experience with backend services, cloud infrastructure, CI/CD, automated testing, observability, and engineering automation.
• Strong proficiency in Python and the ability to work effectively across backend services, infrastructure, tooling, and operational workflows.
• Good understanding of reliability, performance, maintainability, scalability, and infrastructure cost tradeoffs.
• Ability to collaborate effectively with ML and product teams and independently drive ambiguous technical work to completion.
• Strong ownership mentality, attention to detail, and a practical approach focused on simplifying and strengthening systems.
• Experience with MLOps workflows covering model training, evaluation, deployment, and monitoring is highly advantageous.
• Experience serving machine-learning models or LLM-powered applications in production is a strong plus.
• Familiarity with experimentation platforms, event pipelines, analytics instrumentation, or feature delivery platforms is beneficial.
• Experience with agent evaluation, prompt versioning, retrieval and search infrastructure, or vector-backed systems is an advantage.
• Experience supporting customer-facing APIs or SaaS platform infrastructure is preferred.
• Strong troubleshooting, communication, and cross-functional collaboration skills.

Benefits

• Opportunity to take foundational ownership of an AI platform and its engineering standards.
• Broad technical scope spanning backend engineering, cloud infrastructure, MLOps, GenAI, observability, and reliability.
• Direct opportunity to influence how ML and GenAI capabilities are brought into production.
• High level of autonomy and ownership in a growing technology environment.
• Opportunity to work closely with ML, product, and engineering teams on high-impact systems.
• Ability to shape scalable infrastructure, deployment practices, and platform architecture from an early stage.
• Fully remote work environment.
• Full-time position within the IT function.