Junior AI / ML Engineer (Generative AI)

We are looking for a Junior AI / ML Engineer (Generative AI) to join our AI & Data projects. This role is designed for an early-career, AI-native engineer who is comfortable using modern GenAI tools and wants to grow on real projects under the guidance of senior ML / AI experts.
You will help rapidly prototype ideas, build small PoCs, and experiment with solutions based on large language models (LLMs) and other generative AI techniques. Your focus will be to implement well-defined tasks, run experiments, and use GenAI tools to speed up coding, analysis, and documentation – while learning how to do this safely and reliably in production-oriented environments.

Required Qualifications:

• 1–2 years of experience in software development, data science, or ML (commercial experience, internships, or strong personal projects).
• Solid basic skills in Python and core data/ML libraries (e.g., NumPy, pandas, Scikit-learn).
• Understanding of fundamental ML concepts (datasets, train/validation split, basic metrics, overfitting).
• Practical familiarity with generative AI / LLMs:
  - regular use of tools like ChatGPT, Copilot or similar for coding and data tasks;
  - basic understanding of prompts, context windows, and common limitations (hallucinations, privacy, bias).
• High motivation to learn, ask questions, and accept feedback.
• Organized and responsible: able to follow a spec, document work, and meet agreed timelines.

Preferred Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• Course work, pet projects, or a thesis related to ML, NLP, or Generative AI.
• Basic exposure to RAG, embeddings, or vector databases (from articles, courses, or small experiments).
• Familiarity with at least one cloud platform (Azure, AWS, or GCP) from labs or online coursesю.
• Experience with Jupyter notebooks, Git, and simple CI/CD or MLOps tools (even at a “student project” level).
• Participation in hackathons, Kaggle competitions, or open-source projects.

Job Responsibilities:

• Rapidly prototype and iterate on ML / GenAI ideas in notebooks and scripts, turning high-level concepts into small PoCs and demo flows.
• Implement well-defined tasks in Python (data preprocessing, basic model training, evaluation scripts, and utility functions) as part of prototypes and production-oriented components.
• Assist in experiments with LLMs and GenAI APIs (prompt variants, small workflows, simple RAG setups) under the guidance of senior engineers, proactively suggesting new options and documenting what works and what doesn’t.
• Use GenAI tools to support your daily work (coding, tests, documentation, exploratory analysis) and capture key findings in a clear, structured way.
• Prepare and clean datasets, run experiments, and summarize results in concise formats (tables, short reports, simple dashboards).
• Help build and maintain demo environments and internal PoCs for ML / GenAI use cases, keeping them reproducible and easy to reuse.
• Follow coding standards, version control practices, and basic quality processes (code reviews, simple tests, logging).
• Actively learn from senior team members, ask questions, and gradually take ownership of small components within larger solutions.

Department/Project Description

You will join the projects that apply generative AI to real business problems: intelligent assistants, smarter search and retrieval, content transformation, and decision-support workflows.
You will work in cross-functional teams alongside experienced ML engineers, data engineers, and cloud specialists, with a strong focus on practical skills, rapid prototyping, and mentoring-driven growth in the GenAI space.