Architect

Data Scientist ROLE PROFILE • Designs and develops scalable machine learning models and AI-driven solutions to address complex business challenges and enhance decision- making processes KEY RESPONSIBILITIE S • Work with large and complex data sets to solve challenging business problems • Collect, clean, and preprocess large datasets for analysis & model training • Perform exploratory data analysis (EDA) to uncover insights and inform model development • Develop, train, and optimize machine learning models using state-of-the-art algorithms and frameworks • Build end-to-end ML pipelines, including data ingestion, transformation, model training, validation, and deployment • Automate workflows for model training, testing, and deployment using CI/CD pipelines and MLOps tools • Collaborate with cross-functional teams to integrate models into applications and deliver end-to-end solutions Professional Experience/Qualifications • 5 years of experience in data science, machine learning, or AI • Expertise in supervised/unsupervised learning, deep learning, NLP, computer vision, or generative AI (e.g., LLMs). • Strong proficiency in Python and/or R; familiarity with SQL for data querying • Ability to build data pipelines (Spark, Airflow, Hadoop) and work with big data tools • Understanding of model serving, API development (FastAPI, Flask), and optimizing model performance for real-time or batch inference. • Knowledge of Docker, Kubernetes, CI/CD pipelines, and tools like MLflow/Kubeflow for model lifecycle management (MLOps) Classified: Internal\ FAB Internal • Experience deploying models on AWS, Google Cloud, Azure, or similar (e.g., Sagemaker, Vertex AI) • Educational qualifications: Master in Computer Science or a related field. Show more Show less

Professional Experience/Qualifications: 5 years of experience in data science, machine learning, or AI. Expertise in supervised/unsupervised learning, deep learning, NLP, computer vision, or generative AI (e.g., LLMs). Strong proficiency in Python and/or R; familiarity with SQL for data querying. Ability to build data pipelines (Spark, Airflow, Hadoop) and work with big data tools. Understanding of model serving, API development (FastAPI, Flask), and optimizing model performance for real-time or batch inference. Knowledge of Docker, Kubernetes, CI/CD pipelines, and tools like MLflow/Kubeflow for model lifecycle management (MLOps). Experience deploying models on AWS, Google Cloud, Azure, or similar (e.g., Sagemaker, Vertex AI). Educational qualifications: Master in Computer Science or a related field.

Work with large and complex data sets to solve challenging business problems. Collect, clean, and preprocess large datasets for analysis & model training. Perform exploratory data analysis (EDA) to uncover insights and inform model development. Develop, train, and optimize machine learning models using state-of-the-art algorithms and frameworks. Build end-to-end ML pipelines, including data ingestion, transformation, model training, validation, and deployment. Automate workflows for model training, testing, and deployment using CI/CD pipelines and MLOps tools. Collaborate with cross-functional teams to integrate models into applications and deliver end-to-end solutions.