AI/ML Engineer, France

Job Description

As an AI/ML Engineer at vector8, you will implement and deploy AI solutions that bridge the gap between research and production. Your work will focus on integrating and fine‑tuning AI models, optimizing model performance, and contributing to enterprise‑grade reliability, security, and scalability.

This Is a Hands‑on Engineering Role Where You Will

• Develop and optimize LLM and VLM‑powered solutions for enterprise use cases
• Develop and optimize TTS, STT and ML models
• Apply software engineering best practices (testing, CI/CD, modular design, documentation)
• Collaborate with cross‑functional teams (data engineers, MLOps, cloud architects, and business stakeholders)
• Contribute to solving real‑world enterprise challenges (security, compliance, legacy system integration)
• Participate in the full lifecycle of AI models, from data exploration to production monitoring

You will work closely with vector8’s engineers and project managers to co‑design AI foundations that enable organizations to scale AI from individual use cases to enterprise‑wide capabilities.

The role is primarily based in Paris, with occasional travel to client sites and collaboration with teams across Europe.

Job Requirements

• 3–5 years of experience in AI/ML engineering, software development, or a related field
• Working knowledge of LLM architectures and training methodologies: Transformers, attention mechanisms, fine‑tuning, RAG, quantization, prompt engineering, model evaluation, bias detection
• Solid understanding of machine learning architectures: fully connected, CNN, LSTM, transformers and classical ML models
• Good software engineering skills
• Proficiency in Python (FastAPI, Pydantic, asyncio, type hints)
• Experience with API development
• Familiarity with modern toolchains (Docker, Kubernetes, Terraform)
• Practical experience with LLM integrations: LLM providers; vector databases (Pinecone, Weaviate, Milvus); model serving (vLLM, TGI, KServe)
• Some exposure to MLOps and production deployments
• Understanding of enterprise considerations: security, compliance, scalability, cost optimization
• Experience with relational and non‑relational databases
• Strong problem‑solving and debugging skills
• Good communication and collaboration skills (fluent in English; French or German is a plus)
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, or a related field
• Exposure to multi‑cloud environments (AWS, Azure, GCP) is advantageous
• Familiarity with code optimisation techniques (e.g., model quantization, parallelisation) is a plus

Job Responsibilities

Software Engineering for AI; AI & ML Integration & API Development; Enterprise AI & MLOps; Collaboration & Teamwork; Innovation & Continuous Improvement. Embrace a continuous improvement mentality to drive innovation.

• End‑to‑End model development
• Implement and deploy distributed, high‑volume, high‑performance, low‑latency machine learning solutions, with a focus on GenAI models, especially LLM integrations and API‑driven architectures
• Contribute to model lifecycle activities: data exploration and cleaning, research for appropriate architectures, implementation, training, optimisation, deployment, monitoring, maintenance in production
• Optimize models for performance, latency, and cost efficiency, especially in LLM serving and inference
• Write clean, modular, well‑documented code in Python (FastAPI, Pydantic, asyncio)
• Apply best practices in testing (unit, integration, end‑to‑end), CI/CD (GitHub Actions, GitLab CI, ArgoCD), observability (logging, monitoring, tracing)
• Ensure security and compliance (data protection, access controls, encryption)
• Integrate models and code into CI/CD pipelines for seamless deployment
• Implement AI‑powered solutions integrated with APIs, microservices, event‑driven architectures
• Develop and contribute to AI pipelines: dataset cleaning, preprocessing, model training, fine‑tuning, RAG, prompt engineering, model evaluation
• Build scalable, secure, cost‑efficient serving infrastructure (FastAPI, vLLM)
• Debug and optimise performance (latency, throughput, token efficiency for Transformers)
• Deploy and monitor AI models in production
• Contribute to MLOps pipelines: model training, fine‑tuning, evaluation; model versioning, lineage tracking; A/B testing, canary deployments
• Support scalability and reliability (auto‑scaling, fault tolerance, disaster recovery)
• Collaborate with data engineers to build data pipelines (batch, streaming, real‑time)
• Work closely with product owners, DevOps, QA in an agile, cross‑functional team
• Share know