Data & AI Engineer

We are looking for a Data & AI Engineer to join our team. This is a hands‑on role that sits at the intersection of AI and engineering. You'll develop, adapt and run the matching models and data pipelines JTA delivers to its clients, bridging the gap between the models we build and the results they produce at scale in production. You'll combine strong AI know-how with solid data engineering skills to make sure our solutions deliver tangible, measurable value.

What you will be doing:

- Develop and improve the machine learning models and matching algorithms used across our data projects, from feature engineering to evaluation.

- Analyse results on real client data, identify errors and patterns, and turn them into concrete improvements.

- Adapt and extend existing models and data pipelines to fit new data, languages and client requirements, writing robust, maintainable code across the stack.

- Run and monitor production pipelines on Cloud, troubleshooting issues across data and models and ensuring solutions perform as intended.

- Optimise code and pipelines for performance and cost on large‑scale datasets.

- Promote best practices in code quality, model development, and lifecycle management within the team.

- Contribute to internal initiatives that strengthen knowledge sharing and technical excellence across JTA's Data Science practice.

Requirements:

- Bachelor's or Master's in Data Science, Computer Science, Software Engineering, or a related field.

- 2+ years of experience in data science, machine learning engineering, or a similar hands‑on technical role.

- Solid understanding of data science and machine learning concepts, with practical experience adapting models to real‑world data and constraints.

- Strong programming skills in Python, including the data science ecosystem (e.g. pandas, Polars, scikit‑learn), with solid software engineering fundamentals (version control, testing, CI/CD).

- Experience with text processing, NLP or fuzzy matching techniques.

- Experience working with large‑scale datasets and efficient data processing frameworks (e.g. Polars, DuckDB, Spark).

- Experience deploying and operating data science solutions in production environments, including working with APIs, cloud platforms, and containerization (e.g., Docker).

- Excellent communication skills, with the ability to explain models and results to non‑technical stakeholders.

- Strong problem‑solving skills and the ability to work independently in ambiguous, fast‑moving situations.

- Flexible Hybrid Work System;

- 15th Month Bonus Salary Policy;

- Participation in International Projects;

- Company‑Paid Certifications;

- Training, career progression and support;

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