Senior GenAI Engineer — LLMs, Multimodal AI, Production

Terabase Energy; Paris; On-site; EUR 90,000 - 150,000; Full time

Benefits offered by this job

Remote flexibility

Career progression

Stock options

Job summary

Terabase Energy in Paris is seeking a Senior Generative AI Engineer to design, develop, and deploy advanced GenAI models and applications. You will work from research to production, mentor juniors, and shape our LLM and multimodal strategy.

Responsibilities include building LLM-powered apps, RAG pipelines, AI agents, and collaborating with product teams to translate business needs into reliable, scalable AI solutions while advancing ML infrastructure.

Qualifications

• MSc/PhD in CS, ML, or equivalent.
• 5+ years in ML/AI, with 2+ years in LLMs/GenAI.
• Proven production deployments of LLM apps (chatbots, RAG, agents).
• Strong transformer, embeddings, and fine‑tuning expertise.
• Proficiency in Python and ML frameworks (PyTorch, TF).
• Experience with MLOps: CI/CD for ML, monitoring, evals.

Responsibilities

• Develop LLM-powered applications and multimodal AI systems.
• Architect retrieval‑augmented generation (RAG) pipelines.
• Design AI agents with perception, reasoning, and action.
• Mentor junior engineers and researchers in GenAI.
• Collaborate with product managers to translate requirements.
• Define evaluation, reliability, and trust standards for GenAI.
• Stay at the forefront of GenAI research and model evaluation.
• Scale ML infrastructure: distributed training, vector DBs, serving.

Skills

ML/AI experience

LLMs/GenAI

Python programming

MLOps

Cross-functional

Education

MSc/PhD in CS/ML

Tools

PyTorch

TensorFlow

LangChain

LangGraph

LlamaIndex

HuggingFace

Vector DBs

Job description

Terabase Energy in Paris is seeking a Senior Generative AI Engineer to design, develop, and deploy advanced GenAI models and applications. You will work from research to production, mentor juniors, and shape our LLM and multimodal strategy.

Responsibilities include building LLM-powered apps, RAG pipelines, AI agents, and collaborating with product teams to translate business needs into reliable, scalable AI solutions while advancing ML infrastructure.