• 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

Desired Candidate Profile

• 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.

Skills

Machine Learning

Data Analysis

Python

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