Ml Engineer

Role & responsibilities

Designing and developing machine learning and deep learning systems.
• Running machine learning tests and experiments to optimize and improve models. • Implementing machine learning algorithms and libraries. • Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress. • Analysing the ML algorithms that could be used to solve a given problem and ranking them by their success probability. • Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect model performance. • Verifying data quality, and/or ensuring it via data cleaning. • Supervising the data acquisition process if more data is needed. • Finding available datasets online that could be used for training. • Defining validation strategies and ensuring that models are robust against various forms of data. • Deploying models to production and maintaining the code base.

Preferred candidate profile

Around 5 years of experience in AI/ML NLP, NLG, LLMs, RAG, Lang Chain, PIL, YOLO, and OpenCV.
• Proficiency with a deep learning framework such as TensorFlow or Keras. • Proficiency with Python and basic libraries for machine learning, such as scikit-learn and pandas. • Experience in data sciences and building models. • Understanding of data structures, data modelling, and software architecture. • Deep knowledge of math, probability, statistics, and algorithms. • Understanding of version control, especially Git, to track and manage changes. • Ability to write robust code in Python. • Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like NLTK, Spacy, OpenCV, YOLO, PIL, scikit-learn). • Excellent communication skills to work within and across teams. • Ability to work in a fast-paced, team-oriented environment.