Senior AI/ML Engineer, France

As a Senior AI/ML Engineer at vector8, you will design, 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 ensuring enterprise‑grade reliability, security, and scalability.

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Hands‑on Engineering Role

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• 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)
  
• Solve real‑world enterprise challenges (security, compliance, legacy system integration)
  
• Own the full lifecycle of AI models, from data exploration to production monitoring
  

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

Job Requirements

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• 5+ years of experience in AI/ML engineering, software development, or a related field
  
• Expertise in LLM architectures and training methodologies:
  
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  • Transformers, attention mechanisms, fine‑tuning, RAG, quantization
    
  • Prompt engineering, model evaluation, bias detection
    
  
  
  
• Strong knowledge of machine learning architectures: fully connected, CNN, LSTM, transformers, and classical ML models
  
• Strong software engineering skills:
  
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  • Proficient in Python (FastAPI, Pydantic, asyncio, type hints)
    
  • Experience with API development
    
  • Familiarity with modern toolchains (Docker, Kubernetes, Terraform)
    
  
  
  
• Hands‑on experience with LLM integrations:
  
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  • LLM providers
    
  • Vector databases (Pinecone, Weaviate, Milvus)
    
  • Model serving (vLLM, TGI, KServe)
    
  
  
  
• Experience with MLOps and production deployments
  
• Understanding of enterprise challenges:
  
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  • Security, compliance, scalability, cost optimization
    
  
  
  
• Experience with relational and non‑relational databases
  
• Strong problem‑solving and debugging skills
  
• Excellent communication and collaboration skills (fluent in English; German is a strong plus)
  
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, or a related field
  
• Experience with multi‑cloud environments (AWS, Azure, GCP)
  
• Experience with code optimization (e.g., model quantization, parallelization)
  

Job Responsibilities

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• End‑to‑end model development
  
• Design, implement, and deploy distributed, high-volume, high‑performance, low‑latency machine‑learning solutions, focusing on GenAI models, especially LLM integrations and API‑driven architectures
  
• Take ownership of models throughout their lifecycle: data exploration and cleaning, reproducible, versioned datasets, state‑of‑the‑art research to identify best architectures, implementation, training, optimization, deployment, monitoring, maintenance, and optimization for performance, latency, and cost efficiency in LLM serving and inference
  
• Write clean, modular, well‑documented Python code (FastAPI, Pydantic, asyncio)
  
• Apply best practices: 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
  
• Design and implement AI‑powered solutions that integrate with APIs, microservices, and event‑driven architectures
  
• Develop and optimize AI pipelines for dataset cleaning, preprocessing, and model training; fine‑tuning; retrieval‑augmented generation; prompt engineering
  
• Model evaluation (benchmarking, bias detection, drift analysis)
  
• Build scalable, secure, cost‑efficient serving infrastructure (FastAPI, vLLM)
  
• Debug and optimize performance (latency, throughput, token efficiency for transformer‑based architectures)
  
• Deploy and monitor AI models in production
  
• Design and implement MLOps pipelines: training, fine‑tuning, evaluation, versioning and lineage tracking, A/B testing, canary deployments
  
• Ensure scalability and reliability (auto‑scaling, fault tolerance, disaster recovery)
  
• Collaborate with data engineers to build data pipelines (batch, streaming, real‑time)
  
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