Ml Engineer

A Machine Learning Engineer is responsible for designing and developing machine learning and deep learning systems, running machine learning tests and experiments, and implementing appropriate ML algorithms. They work closely with data scientists and are primarily involved in handling large-scale data sets, creating scalable and efficient algorithms, and ensuring that the models are deployed successfully into production. This role requires a blend of data engineering, LLM building knowledge, and practical application of machine learning techniques and algorithms.

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.