AI Engineer

Jobtailor; Paris; Sur place; EUR 60 000 - 90 000; Plein temps

Résumé du poste

Jobtailor is seeking an ML Engineer/Data Scientist to design and optimize the agentic stack for conversational and autonomous agents in Paris. The role focuses on building end-to-end LLM features, model orchestration, and robust ML infrastructure, with emphasis on observability, evaluation, and scalable deployment.

The ideal candidate has 2–5 years of ML/DS experience, strong Python skills, familiarity with LangChain/LangGraph, and a solid grounding in NLP and evaluation methodologies.

Qualifications

• 2–5 years of experience in ML Engineering or Data Science.
• Strong proficiency in Python.
• Experience with lower-level languages such as Java or C is a plus; scripting-only profiles are not sufficient.
• Solid foundation in ML, including model types, data science best practices, and evaluation methods.
• Strong understanding of LLMs, including prompt design, evaluation, and cost/performance trade-offs.
• Experience with agentic systems, including tool execution, workflows, memory architectures, and LangChain / LangGraph or equivalent.
• Exposure to MCP (Model Context Protocol), DeepEval, FastAPI, and Uvicorn.
• Understanding of NLP concepts, including transcription, embeddings, and language understanding.
• Intellectual curiosity and strong ownership/communication skills.

Responsabilités

• Design, build, and optimize the agentic stack for conversational and autonomous agents.
• Architect and maintain end-to-end LLM features, including model orchestration, routing, monitoring, logging, and evaluation.
• Develop and improve ML infrastructure, including observability, load balancing, scaling, and deployment workflows.
• Build datasets and evaluation pipelines for ASR, LLM, and agentic systems.
• Collaborate with Software Engineers to establish scalable, observable, and automated infrastructure around ML services.
• Collaborate with Product Managers to assess feasibility, refine specifications, and reliably deliver ML features.
• Contribute to the technical strategy, model and tool selection, architecture decisions, and roadmap for the agentic platform.
• Stay current with research, frameworks, and open-source developments in LLMs, agentic systems, and evaluation methodologies.
• Participate in the recruitment process, including screening calls, onsite case studies, team-fit interviews, final interviews, and reference checks.

Connaissances

Python proficiency

ML fundamentals

LLMs

Agentic systems

Prompt design

Evaluation methods

Memory architectures

NLP concepts

Ownership/communication

Observation/cost trade-offs

Outils

LangChain

LangGraph

MCP

DeepEval

FastAPI

Uvicorn

Description du poste

• Design, build, and optimize the agentic stack for conversational and autonomous agents
• Architect and maintain end-to-end LLM features, including model orchestration, routing, monitoring, logging, and evaluation
• Develop and improve ML infrastructure, including observability, load balancing, scaling, and deployment workflows
• Build datasets and evaluation pipelines for ASR, LLM, and agentic systems
• Collaborate with Software Engineers to establish scalable, observable, and automated infrastructure around ML services
• Collaborate with Product Managers to assess feasibility, refine specifications, and reliably deliver ML features
• Contribute to the technical strategy, model and tool selection, architecture decisions, and roadmap for the agentic platform
• Stay current with research, frameworks, and open-source developments in LLMs, agentic systems, and evaluation methodologies
• Participate in the recruitment process, including screening calls, onsite case studies, team-fit interviews, final interviews, and, when applicable, reference checks

Requirements

• 2–5 years of experience in ML Engineering or Data Science
• Strong proficiency in Python
• Experience with lower-level languages such as Java or C is a plus; a purely scripting-focused profile will not be sufficient
• Solid foundation in ML, including model types, data science best practices, and evaluation methods
• Strong understanding of LLMs, including prompt design, evaluation, and cost/performance trade-offs
• Experience with agentic systems, including tool execution, workflows, memory architectures, and LangChain / LangGraph or equivalent
• Exposure to MCP (Model Context Protocol), DeepEval, FastAPI, and Uvicorn
• Understanding of NLP concepts, including transcription, embeddings, and language understanding
• Intellectual curiosity
• Strong sense of ownership and communication skills

Core Competencies

Demonstrates expertise in Machine Learning Engineering and Data Science, with a strong proficiency in Python and a solid foundation in LLMs and agentic systems. Capable of architecting and optimizing ML infrastructure while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

• Machine Learning Engineering
• Python Proficiency
• LLM Understanding
• Agentic Systems Experience
• Model Evaluation Methods

Hard Skills

• Machine Learning
• Data Science
• Model Orchestration
• Prompt Design
• Evaluation Methodologies
• Load Balancing
• Scaling
• Deployment Workflows
• NLP Concepts
• Tool Execution

Soft Skills

• Intellectual Curiosity
• Strong Sense of Ownership
• Communication Skills

Industry Keywords

• Conversational Agents
• Autonomous Agents
• Observability
• Data Science Best Practices
• Evaluation Pipelines

Tools & Technologies

• LangChain
• LangGraph
• MCP
• DeepEval
• FastAPI
• Uvicorn