Machine Learning Engineer

We are seeking a skilled Machine Learning Engineer to join a dynamic team in Bengaluru. In this role, you will design, develop, and deploy scalable machine learning models to solve complex business challenges within the finance and accounting sector. You will work in a hybrid environment, collaborating with cross-functional teams to integrate advanced AI solutions into enterprise-grade systems.

Our client is a leading global professional services firm operating at the intersection of finance, technology, and consulting. They are renowned for their commitment to innovation and digital transformation, providing high-impact solutions to a diverse range of international clients. The organization fosters a collaborative, inclusive culture that prioritizes continuous learning and professional growth.

Responsibilities

• Design and implement end-to-end machine learning pipelines using Python and modern frameworks.
• Develop and deploy deep learning models for complex data analysis and predictive modeling.
• Architect and maintain scalable MLOps workflows to ensure model reliability and performance.
• Collaborate with data engineers to optimize data processing using Spark, Hadoop, and Redshift.
• Apply advanced NLP and recommendation system techniques to enhance business intelligence capabilities.
• Conduct system design reviews to ensure high availability and efficiency of AI-driven applications.
• Monitor and refine model performance in production environments to meet business requirements.

Requirements

• 5-8 years of professional experience in machine learning engineering or data science.
• Strong proficiency in Python and SQL for data manipulation and model development.
• Hands-on experience with deep learning frameworks including PyTorch, TensorFlow, or JAX.
• Proven expertise in MLOps practices and cloud-based infrastructure, specifically AWS.
• Solid understanding of system design principles for large-scale data applications.
• Experience working with big data technologies such as Spark, Hadoop, and Redshift.
• Strong background in implementing NLP and recommendation systems in a production setting.