Key Responsibilities

• Design, develop, and deploy machine learning and deep learning models to solve business and operational challenges.
• Apply advanced statistical, machine learning, and AI techniques to extract insights from structured and unstructured datasets.
• Perform data acquisition, exploration, cleansing, preprocessing, and feature engineering activities.
• Evaluate and select appropriate algorithms, architectures, and modelling approaches based on business requirements.
• Build, optimize, and maintain end-to-end machine learning pipelines.
• Fine-tune and enhance model performance through experimentation, testing, and continuous improvement.
• Identify new data sources and automate data collection and preparation processes where possible.
• Develop solutions across predictive analytics, natural language processing (NLP), and AI-driven automation use cases.
• Integrate machine learning models into production environments using APIs and containerized deployment approaches.
• Collaborate with engineering and DevOps teams to support model deployment, monitoring, and operational excellence.
• Contribute to research and innovation initiatives, evaluating emerging AI and machine learning technologies.
• Work closely with stakeholders to translate business requirements into scalable data science solutions.
• Present insights and technical recommendations to both technical and non-technical audiences.
• Support solution rollout, user adoption, and ongoing optimization activities.

Required Qualifications

• Bachelor's degree in Data Science, Artificial Intelligence, Computer Science, Mathematics, Engineering, Business Analytics, or a related discipline.
• Relevant industry certifications in AI, Machine Learning, Data Science, or Cloud Technologies are advantageous.

Experience

• 7–10 years of experience in Data Science, Machine Learning, Artificial Intelligence, or related analytical roles.
• Proven experience delivering machine learning solutions in enterprise environments.
• Demonstrated experience working across the complete machine learning lifecycle, from data preparation through deployment and monitoring.

Technical Skills

Machine Learning & AI

• Strong understanding of machine learning and deep learning concepts and methodologies.
• Experience developing models for both structured and unstructured datasets.
• Hands-on experience with:
  • TensorFlow
  • PyTorch
  • Scikit-learn
• Knowledge of model evaluation, optimization, and performance tuning techniques.

Natural Language Processing (NLP)

• Hands-on experience building NLP solutions using:
  • Hugging Face
  • spaCy
  • Gensim
• Experience working with transformer-based architectures and modern NLP frameworks.

Programming & Data Engineering

• Advanced Python programming skills.
• Knowledge of Java or R is desirable.
• Strong experience with:
  • SQL
  • Pandas
  • Apache Spark