Data Science Engineer (NLP)

About the Role

Responsibilities:

• Apply NLP techniques to preprocess and analyze large-scale textual data, developing and fine-tuning Large Language Models (LLMs) and multimodal models to generate actionable business insights.
• Design, build, and maintain end-to-end machine learning pipelines—including data ingestion, cleaning, feature engineering, model training, evaluation, deployment, and monitoring
• Lead the deployment of ML models in production environments with a focus on scalability, reliability, availability, and low-latency inference, leveraging cloud infrastructure for optimal performance.
• Collaborate with business and technical stakeholders to identify AI opportunities, align initiatives with organizational goals, and communicate insights effectively through data analysis and visualization.
• Stay abreast of the latest AI advancements, particularly in multimodal AI, to continuously integrate cutting-edge technologies into solutions.
• Explore the use of agentic AI to automate detection and monitoring within risk management systems, improving accuracy and response times.

Requirements:

• Minimum 4 years of industry experience in AI/ML, preferably focused on NLP and/or multimodal AI, with a Master’s degree or higher in Computer Science, Data Science, or related fields.
• Proficient in big data technologies (e.g., Apache Spark, Hadoop, Kafka, VectorDB) or equivalent platforms.
• Skilled in programming languages such as Python or Java, with hands-on experience in ML/NLP libraries and deep learning frameworks (TensorFlow, PyTorch, Scikit-learn, SpaCy, NLTK).
• Strong understanding of modern machine learning and deep learning techniques, including transformer architectures (BERT, GPT), hyperparameter optimization, and methods for handling imbalanced datasets.
• Experience optimizing and deploying ML models for low-latency inference in production, familiar with end-to-end ML deployment processes including version control (Git), continuous integration/continuous deployment (CI/CD), and managing multiple environments (dev, QA, staging, production).

Preferred

• Experience with productionising agentic AI systems or similar autonomous AI solutions is a plus.
• Prior experience in e-commerce or technology sectors is highly desirable.

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