- Company Name
- LumApps
- Job Title
- Senior AI Engineer (F/M) - Paris/Lyon/Krakow
- Job Description
-
**Job Title**
Senior AI Engineer
**Role Summary**
Design, build, deploy, and maintain AI-driven features for an enterprise collaboration platform. Work with cross‑functional product squads to integrate modern generative AI models, develop MLOps pipelines, and ensure scalable, fair, and cost‑effective AI services in a cloud‑native environment.
**Expectations**
- 5+ years of professional backend development experience.
- Proven track record delivering production AI/ML solutions.
- Ability to prototype, iterate, and ship AI models quickly while meeting business objectives.
- Strong understanding of the ethical, fairness, and bias considerations in AI systems.
**Key Responsibilities**
- Integrate cutting‑edge AI models (generative, large language models, and traditional ML) into product features.
- Deploy, monitor, and maintain AI services on GCP/AWS using IaC (Terraform, Spacelift).
- Build and automate end‑to‑end MLOps pipelines (data ingestion, training, validation, deployment).
- Optimize model performance, cost, and latency in production environments.
- Collaborate with product, data, and ops teams to define AI feature requirements and success metrics.
- Ensure AI systems adhere to fairness, transparency, and ethical guidelines.
**Required Skills**
- Strong programming in Python; fluency in Java.
- Deep expertise in generative AI, deep learning, and classic ML (classification, regression, clustering).
- Proficiency with TensorFlow, PyTorch, scikit‑learn, and related libraries.
- Experience with Google Vertex AI, AWS SageMaker/Strands, and deployment of LLMs.
- Cloud infrastructure knowledge: GCP, AWS; IaC with Terraform, Spacelift.
- Container orchestration with Kubernetes (EKS/GKE); package management with Helm, GitOps (FluxCD/ArgoCD).
- Database familiarity: PostgreSQL, MySQL, ClickHouse, BigQuery, Redis, Elasticsearch.
- Monitoring/logging with Prometheus, Datadog, Grafana.
- Familiarity with MLOps practices, model versioning, experiment tracking, and CI/CD for ML.
- Knowledge of techniques to extract high‑quality outputs from large language models.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Data Science, Machine Learning, or a closely related field.
- Optional certifications (e.g., Google Cloud Professional ML Engineer, AWS Certified Machine Learning) are a plus but not mandatory.
---
Tassin-la-demi-lune, France
Hybrid
Senior
05-11-2025