Job
Description
Key Skills: Machine Learning, Data Science, Azure, Python, Hadoop. Roles and Responsibilities: Strong understanding of Math, Statistics, and the theoretical foundations of Statistical & Machine Learning, including Parametric and Non-parametric models. Apply advanced data mining techniques to curate, process, and transform raw data into reliable datasets. Use various statistical techniques and ML methods to perform predictive modeling/classification for problems related to clients, distribution, sales, client profiles, and segmentation, and provide actionable insights for business decision-making. Demonstrate expertise in the full Machine Learning lifecycle--feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loops. Proficiency in Python visualization libraries such as matplotlib and seaborn. Experience with cloud computing infrastructure like Azure, including Machine Learning Studio, Azure Data Factory, Synapse, Python, and PySpark. Ability to develop, test, and deploy models on cloud/web platforms. Excellent knowledge of Deep Learning Architectures, including Convolutional Neural Networks and Transformer/LLM Foundation Models. Strong expertise in supervised and adversarial learning techniques. Robust working knowledge of deep learning frameworks such as TensorFlow, Keras, and PyTorch. Excellent Python coding skills. Experience with version control tools (Git, GitHub/GitLab) and data version control. Experience in end-to-end model deployment and productionization. Demonstrated proficiency in deploying, scaling, and optimizing ML models in production environments with low latency, high availability, and cost efficiency. Skilled in model interpretability and CI/CD for ML using tools like MLflow and Kubeflow, with the ability to implement automated monitoring, logging, and retraining strategies. Experience Requirement: 5-12 years of experience in designing and deploying deep learning and machine learning solutions. Proven track record of delivering AI/ML solutions in real-world business applications at scale. Hands-on experience working in cross-functional teams including data engineers, product managers, and business stakeholders. Experience mentoring junior data scientists and providing technical leadership within a data science team. Experience working with big data tools and environments such as Hadoop, Spark, or Databricks is a plus. Prior experience in managing model lifecycle in enterprise production environments including drift detection and retraining pipelines. Education: B.Tech.
