Job Summary:

We are looking for a versatile and results-driven Data Scientist / Machine Learning Developer with 7+ years of experience to join our dynamic team. The ideal candidate will have a strong background in both data science and machine learning, capable of handling end-to-end processes from data analysis and feature engineering to model deployment and monitoring. This role demands a proactive and collaborative mindset, working closely with product owners and engineering teams to deliver scalable, production-ready ML solutions. You will take ownership of the entire model lifecycle driving experimentation, validation, deployment, and continuous optimization to create high-impact, AI-powered business value.

In this Role, Your Responsibilities Will Be:

• Develop, train and deploy machine learning, deep learning AI models for a variety of business use cases such as classification, prediction, recommendation, NLP and Image Processing.
• Design and implement end-to-end ML workflows from data ingestion and preprocessing to model deployment and monitoring.
• Collect, clean, and preprocess structured and unstructured data from multiple sources using industry-standard techniques such as normalization, feature engineering, dimensionality reduction, and optimization.
• Perform exploratory data analysis (EDA) to identify patterns, correlations, and actionable insights.
• Apply advanced knowledge of machine learning algorithms including regression, classification, clustering, decision trees, ensemble methods, and neural networks.
• Use Azure ML Studio, TensorFlow, PyTorch, and other ML frameworks to implement and optimize model architectures.
• Perform hyperparameter tuning, cross-validation, and performance evaluation using industry-standard metrics to ensure model robustness and accuracy.
• Integrate models and services into business applications through RESTful APIs developed using FastAPI, Flask or Django.
• Build and maintain scalable and reusable ML components and pipelines using Azure ML Studio, Kubeflow, and MLflow.
• Enforce and integrate AI guardrails: bias mitigation, security practices, explainability, compliance with ethical and regulatory standards.
• Deploy models in production using Docker and Kubernetes, ensuring scalability, high availability, and fault tolerance.
• Utilize Azure AI services and infrastructure for development, training, inferencing, and model lifecycle management.
• Support and collaborate on the integration of large language models (LLMs), embeddings, vector databases, and RAG techniques where applicable.
• Monitor deployed models for drift, performance degradation, and data quality issues, and implement retraining workflows as needed.
• Collaborate with cross-functional teams including software engineers, product managers, business analysts, and architects to define and deliver AI-driven solutions.
• Communicate complex ML concepts, model outputs, and technical findings clearly to both technical and non-technical stakeholders.
• Stay current with the latest research, trends, and advancements in AI/ML and evaluate new tools and frameworks for potential adoption.
• Maintain comprehensive documentation of data pipelines, model architectures, training configurations, deployment steps, and experiment results.
• Drive innovation through experimentation, rapid prototyping, and the development of future-ready AI components and best practices.
• Write modular, maintainable, and production-ready code in Python with proper documentation and version control.
• Contribute to building reusable components and ML accelerators.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field over 7+ years.
• Proven experience as a Data Scientist, ML Developer, or in a similar role.
• Strong command of Python and ML libraries (e.g., Azure ML Studio, scikit-learn, TensorFlow, PyTorch, XGBoost).
• Data Engineering: Experience with ETL/ELT pipelines, data ingestion, transformation, and orchestration (Airflow, Dataflow, Composer).
• ML Model Development: Strong grasp of statistical modelling, supervised/unsupervised learning, time-series forecasting, and NLP.
• Proficiency in Python
• Strong knowledge of machine learning algorithms, frameworks (e.g., TensorFlow, PyTorch, scikit-learn), and statistical analysis techniques.
• Proficiency in programming languages such as Python, R, or SQL.
• Experience with data preprocessing, feature engineering, and model evaluation techniques.
• MLOps & Deployment: Hands-on experience with CI/CD pipelines, model monitoring, and version control.
• Familiarity with cloud platforms (e.g., Azure (Primarily), AWS and deployment tools.
• Knowledge of DevOps platform.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.

Preferred Qualifications:

• Proficiency in Python, with libraries like pandas, NumPy, scikit-learn, spacy, NLTK and Tensor Flow, Pytorch
• Knowledge of natural language processing (NLP) and custom/computer, YoLo vision techniques.
• Experience with Graph ML, reinforcement learning, or causal inference modeling.
• Familiarity with marketing analytics, attribution modelling, and A/B testing methodologies.
• Working knowledge of BI tools for integrating ML insights into dashboards.
• Hands on MLOps experience, with an appreciation of the end-to-end CI/CD process
• Familiarity with DevOps practices and CI/CD pipelines.
• Experience with big data technologies (e.g., Hadoop, Spark) is added advantage
• Certifications in AI/ML