Lead AI Engineer

Jobtailor; Paris; Sur place; EUR 90 000 - 140 000; Plein temps

Résumé du poste

Jobtailor is seeking an experienced AI Engineer to design and deploy AI solutions at scale in a Paris-based environment. You will develop LLM/VLM applications, implement hybrid search and RAG pipelines, and build agentic systems that interact with internal tools and external data sources.

The role requires strong Python and SQL coding skills, English communication, and a rigorous approach to experimentation and metrics.

Qualifications

• 5–10 years of experience in Machine Learning, Deep Learning, or AI Engineering.
• Hands-on experience with LLM/VLM application development: fine-tuning, prompt engineering, tool integration, evaluation, and benchmarking.
• Experience with information retrieval and modern retrieval stacks: hybrid search, large-scale embeddings, vector databases, and RAG architectures.
• Experience building or orchestrating agentic AI systems.
• Strong scientific rigor: ability to design metrics aligned with product goals, run controlled experiments, and communicate results to guide technical and product decisions.
• Experience with large-scale production applications (monitoring, reliability, performance, observability).
• Strong coding skills in Python and proficiency in SQL.
• Proficient written and verbal communication skills in English.

Responsabilités

• Design and implement AI solutions using LLMs, RAG, and agentic frameworks for complex business challenges.
• Build and maintain information retrieval pipelines, including hybrid search, vector databases, and multi-stage re-ranking.
• Develop and fine-tune LLMs and VLMs for domain tasks like product categorization and semantic search.
• Build and orchestrate agentic AI systems that interact with internal tools and external data sources.
• Write production-ready code and deploy AI systems at scale in live environments.
• Define and track evaluation metrics; run controlled experiments and communicate results clearly.
• Collaborate with engineers, product managers, and stakeholders to frame problems scientifically and business-wise.
• Investigate and resolve production issues; ensure reliability and observability of AI systems.
• Keep up to date with Generative AI trends and scalable ML.

Connaissances

ML & DL

LLM/VLM development

Information retrieval

Agentic AI systems

Python

SQL

English communication

Experimentation & metrics

Description du poste

Responsibilities

• Design and implement AI solutions using Large Language Models (LLMs), retrieval-augmented generation (RAG), and agentic frameworks to address complex business challenges
• Build and maintain information retrieval pipelines, including hybrid search (sparse + dense), vector databases, and multi-stage re-ranking systems
• Develop and fine-tune LLMs and vision-language models (VLMs) for domain-specific tasks such as product categorization, attribute extraction, and semantic search
• Build and orchestrate agentic AI systems that interact with internal tools and external data sources
• Write production-ready code and deploy AI systems at scale in live environments
• Define and track evaluation metrics aligned with product and business goals; run controlled experiments (A/B tests, benchmarks) and clearly communicate results
• Collaborate with software engineers, product managers, and business stakeholders to frame problems from both scientific and business perspectives
• Investigate and resolve production issues; ensure reliability, observability, and performance of AI systems
• Keep up to date with the latest AI trends (Generative AI, agentic systems, scalable ML, etc.)

Qualifications

• 5–10 years of experience in Machine Learning, Deep Learning, or AI Engineering
• Hands-on experience with LLM/VLM application development: fine-tuning, prompt engineering, tool integration, evaluation, and benchmarking
• Experience with information retrieval and modern retrieval stacks: hybrid search, large-scale embeddings, vector databases, and RAG architectures
• Experience building or orchestrating agentic AI systems
• Strong scientific rigor: ability to design metrics aligned with product goals, run controlled experiments, and communicate results to guide technical and product decisions
• Experience with large-scale production applications (monitoring, reliability, performance, observability)
• Strong coding skills in Python and proficiency in SQL
• Proficient written and verbal communication skills in English
• Growth mindset: continuous improvement of technical and soft skills