wizeline26d ago

Senior ML/AI Engineer

Barcelonasenior

Data ScienceOtherMachine Learning EngineerData

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Quick Summary

Overview

We are: Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI,

Technical Tools

Data ScienceOtherMachine Learning EngineerData

Responsibilities

~1 min read

→Collaborate with data scientists and engineers to orchestrate LLMs and tools into complex AI workflows.
→Implement optimized vector storage and indexing systems for NLP.
→Develop tools and frameworks for prompt management, automated evaluations, and observability.
→Monitor and improve agentic systems performance for accuracy and efficiency.
→Provide ongoing support, troubleshoot issues, and implement updates for ML solutions
→Build and maintain data processing pipelines for high volumes of structured and unstructured data.
→Integrate third-party AI APIs (internal and external) to extend the functionality of systems.
→Stay updated on GenAI, NLP, ML, and IR technologies, incorporating best practices and leveraging cloud infrastructure for efficiency.
→Build and extend internal products on top of the LangChain ecosystem (LangGraph, LangSmith) to support prompt management, evaluation, and agent orchestration across the team.

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related STEM field or equivalent work experience.
4+ years of industrial experience in a machine learning engineering or data engineering role.
Strong programming skills in Python and/or another high-level language commonly used in machine learning.
Experience deploying LLMs, implementing automated evaluation pipelines (LLM-as-a-judge), and architecting multi-agent systems that utilize tool-calling and long-term memory to solve non-linear problems.

AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
Familiarity with cloud-based infrastructure and services (e.g., AWS and GCP), Docker, and the Git version control system
Familiarity with consuming and integrating APIs in a reliable and secure manner.