Job
Description
Roles and Responsibilities: • Design, develop, and deploy machine learning models to solve real-world business problems. • Develop production-quality machine learning pipelines and frameworks using Python and Java. • Collaborate with data scientists, engineers, and business teams to define data requirements, model specifications, and system architecture. • Preprocess and clean large datasets, perform feature engineering, and ensure data quality for ML applications. • Implement, optimize, and deploy algorithms, ensuring scalability and performance in production systems. • Work with cloud-based platforms (AWS, GCP, Azure) for deploying and scaling machine learning models. • Stay current with the latest machine learning research and trends, and propose innovative solutions and techniques. • Document processes, models, and code to ensure reproducibility, maintainability, and knowledge sharing. Required Skills & Qualifications: • 5-6 years of professional experience in software engineering, with at least 2-3 years of hands-on experience in machine learning and data science. • Proficiency in Python for machine learning, data analysis, and algorithm development. • Fair experience with Java, including the development of production-level applications and integration with ML models. • Familiarity with data preprocessing techniques, including feature extraction, cleaning, normalization, and transformation. • Strong experience with SQL and working with large datasets from databases and data lakes. • Proficiency in cloud platforms (AWS, GCP, or Azure) for deploying machine learning models and managing infrastructure. • Strong knowledge of version control systems like Git and experience with CI/CD pipelines for machine learning models. • Strong problem-solving and debugging skills. • Ability to work independently and in a collaborative team environment. • Excellent communication skills for technical documentation and presenting solutions to stakeholders. Desired Skills: • Familiarity with big data technologies such as Spark, or Kafka. • Experience with DevOps practices for automating ML workflows. • Experience in deploying ML models using Docker, Kubernetes, or similar containerization technologies.
