Data Scientist - Generative AI & LLM - IT/Analytics

Job Overview:

We are looking for an experienced Data Scientist with expertise in Artificial Intelligence, Machine Learning, Generative AI, and advanced data analytics. The ideal candidate will be responsible for developing predictive models, implementing AI-driven solutions, and leveraging large language models to solve complex business problems. The role involves working on end-to-end data science initiatives, including data analysis, model development, experimentation, deployment, and performance monitoring. The candidate will collaborate with cross-functional teams to build scalable AI solutions using Python, modern machine learning frameworks, cloud platforms, and enterprise AI technologies.

Key Responsibilities:

- Design, develop, and implement machine learning and AI models to address complex business challenges.

- Apply statistical modelling, predictive analytics, and data science techniques to extract meaningful insights from structured and unstructured datasets.

- Develop and deploy Generative AI and LLM-based applications for enterprise use cases.

- Build Retrieval-Augmented Generation (RAG) solutions using embeddings, vector databases, and prompt engineering techniques.

- Work on Agentic AI applications, intelligent agents, and automated workflows to support enterprise business processes.

- Develop and optimize machine learning models using Python and frameworks such as PyTorch, TensorFlow, and Scikit-learn.

- Perform data exploration, feature engineering, model training, validation, and performance evaluation.

- Work with Natural Language Processing (NLP) techniques and large language models to develop intelligent data-driven applications.

- Leverage cloud platforms such as Microsoft Azure and AWS to develop, deploy, and manage AI/ML solutions.

- Work with Databricks, Apache Spark, and distributed data processing technologies for large-scale data analytics and model development.

- Implement MLOps and LLMOps practices to streamline model deployment, monitoring, evaluation, and lifecycle management.

- Monitor model performance, identify model drift, and implement improvements to maintain accuracy and reliability.

- Apply Responsible AI principles, ensuring model explainability, transparency, fairness, security, and governance.

- Collaborate with data engineers, software developers, business analysts, and stakeholders to translate business requirements into AI-driven solutions.

- Prepare technical documentation, model evaluation reports, and presentations to communicate findings and recommendations.

- Continuously evaluate emerging AI/ML technologies and recommend innovative approaches to improve enterprise AI capabilities.

Required Skills and Qualifications:

- Experience in Data Science, Machine Learning, Artificial Intelligence, or related fields.

- Strong programming knowledge in Python.

- Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn.

- Good understanding of statistical modelling, predictive modelling, and data analytics.

- Experience developing Generative AI and LLM-based applications.

- Knowledge of NLP, prompt engineering, RAG architectures, embeddings, and vector databases.

- Understanding of Agentic AI frameworks, AI agents, and intelligent workflow automation.

- Experience working with cloud platforms such as Azure or AWS.

- Knowledge of Databricks, Apache Spark, and large-scale data processing.

- Understanding of MLOps, LLMOps, model deployment, and model monitoring.

- Knowledge of Responsible AI, model governance, explainability, and evaluation techniques.

- Strong analytical, problem-solving, and communication skills.

- Ability to collaborate effectively with cross-functional and business teams.

Key Skills:

- Data Science and Machine Learning

- Artificial Intelligence and Generative AI

- Agentic AI and Large Language Models

- Python, PyTorch, TensorFlow, Scikit-learn

- NLP and Predictive Modelling

- Statistical Modelling and Data Analytics

- RAG and Prompt Engineering

- Vector Databases and Embeddings

- Azure, AWS, Databricks, and Spark

- MLOps and LLMOps

- Responsible AI and Model Governance

- Model Explainability and Monitoring

Skills

Data Science, Machine Learning, Artificial Intelligence, Analytics, Data Analytics, Python, Statistics, Data Modeling, Data Scientist, Generative AI, Statistical Modeling