Senior Machine Learning Engineer

As a Senior Machine Learning (AI) Engineer, you will be responsible for designing, developing, and deploying advanced machine learning solutions across various domains, including Personalization, Recommender engines, and LLMs, which integrate with data pipelines and other data sources. This role involves end-to-end project ownership, from data preprocessing to the creation of service APIs, and offers opportunities to work on cutting-edge AI technologies.

Help us shape the future of communication by:

• Providing guidance to junior and med-level team members, sharing knowledge, and offering advice on machine learning and software engineering practices and approaches.
• Establishing and maintaining robust communication channels with other cross-functional teams to facilitate the integration of machine learning solutions into other Unifonic products.
• Developing and optimizing reliable and scalable personalization and recommendation
  machine learning models and creating/exposing service APIs using frameworks such as Flask, FastAPIs, or other relevant frameworks.
• Collect, analyze, and create the required scalable and reliable
  data pipelines from extensive datasets in real-time, near-real-time, or batch processing modes.
• Implementing proof of concepts and prototypes to demonstrate the potential for new AI use cases and innovations.
• Reviewing the code of other team members and suggesting improvements to ensure the SOLID principles and clean architecture.
• Assisting in the project documentation and demos.
• Keeping current with the latest machine learning research papers and AI trends, such as Generative AI.

What you'll bring:

• Hands-on 3-5 years
  of relevant work experience as a Machine Learning Engineer, hands-on Python, personalization and recommendation engines are highly recommended.
• Excellent analytical abilities, with the capacity to collect, organize, and analyze large datasets to glean valuable insights.
• Bachelor's or Master's degree in Engineering, Computer Science, or a related field (or equivalent experience).
• End-to-end experience in training, evaluating, testing, and deploying scalable machine learning products in production.
• Write world-class code in Python (SOLID principles), considering the best software engineering practices, i.e. data structures, algorithms, and data modeling
• Solid experience in ML frameworks such as NumPy, Pandas, Scikit-Learn, PyTorch, Keras, BERT, Tensorflow, and similar.
• Recommendation Systems
  :
  Deep understanding of various recommendation algorithms (Collaborative Filtering - user-based, item-based; Content-Based Filtering; Hybrid approaches). Knowledge of matrix factorization techniques (SVD, ALS), graph-based methods, and deep learning/LLMs for recommendations.
• Supervised Learning: Regression (Linear, Logistic), Classification (SVM, Decision Trees, Random Forests, Gradient Boosting Machines like XGBoost/LightGBM).
• Unsupervised Learning: Clustering (K-Means, DBSCAN, Hierarchical Clustering), Dimensionality Reduction (PCA, t-SNE).
• Deep Learning:
  Strong grasp of Neural Networks (CNNs, RNNs, LSTMs, Transformers).
• Familiarity with MLOps
  best practices, e.g. Model deployment and reproducible research, Basic knowledge of Kubernetes, Docker, and CI/CD concepts.
• Data science best practices:
  Needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, and dimensionality reduction.
• Excellent understanding of Machine learning models like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.
• Experience with the Hugging Face libraries (i.e. transformers).
• Experience fine-tuning
  pre-trained models and using vector search
  to enhance LLMs results.
• Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques.
• Familiar with Agile methodologies, i.e. scrum and kanban.
• Ability to develop high-level architecture and low-level design, End-to-end for a specific project.
• Experience in event sourcing patterns and tools, i.e. Kafka, RabbitMQ, or similar.
• Experience with LLM frameworks
  (i.e. LangChain) and prompt engineering techniques.
• General knowledge of Data warehouse tools
  e.g. Vertica is a plus.