AI/ML Expert

Classic Machine Learning solutions: Designing and developing robust ML models for real-world problems. Focus areas include predictive analytics, recommendation systems, credit scoring, churn prediction, and computer vision.

Data mastery: You are the architect of the data. You will handle complex data cleaning, manipulation, and preparation to ensure high-quality inputs for model training.

Solution architecture: Selecting the “best tool for the job”—whether that is a simple regression model or a complex neural network—based on client constraints and business value.

Tech consulting: You will play a key role in the pre-sales process. This includes meeting with clients to understand their business needs, actively participating in workshops to discover AI potential, estimating workload/costs, and translating abstract requirements into concrete, actionable technical plans.

Project delivery: Taking ownership of the AI component in projects from conceptualisation to delivery, ensuring they align with the client’s strategy and Miquido’s standards.

Strategy: Working with stakeholders to identify opportunities for AI-driven innovation

Strong Proficiency in Python: Specifically focused on Data Science libraries (pandas, scikit-learn, NumPy, matplotlib/seaborn). At least 5 years of commercial experience, ideally 8+.

Classic ML Expertise: Very good knowledge of Machine Learning algorithms (Regression, Classification, Clustering, Random Forests, Gradient Boosting) and solid model validation techniques.

Data Manipulation Skills: You actually enjoy the “dirty work” of data science—cleaning, wrangling, and structuring large datasets is second nature to you.

SQL Mastery: Very good knowledge of SQL for data extraction and analysis.

Consulting mindset: A business-oriented approach with the ability to present technical solutions to non-technical audiences. You are comfortable estimating time/budget and arguing for the best solution in a sales context.

Workshop experience: Ability to actively participate in discovery workshops and cooperate with various stakeholders to manage changing priorities.

Development environment: Familiarity with Jupyter Notebooks and Git.

Language skills: Very good command of written and spoken Polish and English (C1 level).

LLM Experience: Understanding of Large Language Models (GPT, Gemini, Claude), prompting, RAG, and fine-tuning.

Software Engineering: Experience developing backend solutions, API integrations, or putting models into production code.

Cloud Proficiency: Experience with AWS, Azure, or GCP.
NLP Background: Experience with statistical learning techniques for Natural Language Processing (entity extraction, summarization, etc.).