ML Consultant/BQML Advisor

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. 

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.  

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing. 

 

About project:

The consultant will act as a trusted advisor and mentor rather than an individual contributor building models. The goal is to help engineering teams understand machine learning fundamentals, review existing work, provide recommendations, and improve overall ML maturity across the organization. This engagement is expected to start as a part time consultancy assignment.

Our client is looking for a senior ML consultant to support an internal AI Platform engineering team that is currently building and training machine learning models in BigQuery ML (BQML). The team has already developed multiple models but lacks practical machine learning expertise needed to properly frame business problems, select algorithms, evaluate model performance, and guide production readiness.

You will be:

• advising software engineering teams on machine learning best practices and solution design,
• helping engineering teams translate business problems into effective machine learning solutions,
• guiding model selection, training, validation, evaluation, and deployment approaches,
• reviewing existing BigQuery ML implementations and recommend technical improvements,
• educating engineers on machine learning concepts, model evaluation techniques, and performance metrics,
• explain concepts such as false positives, false negatives, precision, recall, and model quality to technical and non-technical audiences,
• conduct technical reviews of existing machine learning models and provide actionable recommendations,
• support engineering teams in interpreting model outputs and making informed technical decisions,
• recommend learning paths, engineering standards, and operational improvements for machine learning adoption,
• contribute to the development of machine learning best practices, governance, and review processes,
• collaborate with engineering teams, architects, and stakeholders to promote consistent and scalable ML adoption,
• serve as an on-demand machine learning expert providing consultations and technical guidance across multiple teams,

Your profile:

• strong commercial experience in machine learning, data science, or applied AI roles,
• deep practical understanding of supervised learning techniques and machine learning fundamentals,
• strong knowledge of model training, validation, evaluation, and performance optimization,
• experience reviewing machine learning solutions and providing technical guidance,
• ability to mentor, coach, and educate software engineering teams,
• strong communication and stakeholder management skills,
• ability to explain complex machine learning concepts to engineers without an ML background,
• experience working collaboratively across multiple engineering teams,
• strong analytical thinking and problem-solving skills,
• ability to balance technical excellence with practical business objectives,

• practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.

• Work from the European Union region and a work permit are required.

Nice to have:

• experience with BigQuery ML (BQML),
• experience working within AdTech or digital advertising environments,
• experience implementing or supporting MLOps practices,
• experience coaching software engineers transitioning into machine learning development,
• experience building machine learning enablement, training, or adoption programs,

• experience in technical consulting, advisory, or architecture-focused roles,
• experience supporting multiple engineering teams simultaneously,
• experience designing or contributing to machine learning governance and review processes,
• background working with analytics platforms or data science ecosystems,

• experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work.

• Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.

 

 

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision