SSE - Machine Learning

Responsibilities:

• Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to deliver AI-driven solutions.
• Ability to do exploratory data analysis, read research papers and state-of-the-art models in literature and implement them.
• Design, develop, and maintain scalable machine learning systems for time series forecasting and general predictive modelling (in both CPU and GPU machines).

Requirements:

• Strong experience in NLP, recommender systems, and machine learning algorithms (classification, regression, clustering, anomaly detection, pattern recognition techniques, deep learning, embeddings, LLMs/RAG/agentic AI).
• Hands-on experience in building ML systems for search and personalisation use cases.
• Experience designing and deploying ML solutions at large scale (billions of records).
• Experience leveraging parallel processing techniques (multithreading, multiprocessing, distributed computing) to build high-performance, scalable machine learning pipelines and optimise large-scale data processing workloads.
• Familiarity with real-time data streaming technologies such as Kafka and Flink.
• Experience working with machine learning algorithms and technologies.
• Experience working on time series problems, implementing existing methods in general, and the ability to develop new solutions (statistical and probabilistic models, deep learning, and transformers).
• Experience working with PyTorch, scikit-learn and time series Python libraries for model training and evaluation experiments.
• Experience in Python and the ability to write production-level code.
• Critical thinking and strong technical knowledge in data structures, algorithms and system design.
• Potential to innovate novel machine learning methods at industry standards and publish at international conferences.
• Exposure to natural language processing and computer vision algorithms.
• Knowledge of data governance and ethical AI principles.