Machine Learning Engineer

About the job

Key Responsibilities Engage with clients to understand their business objectives and challenges, providing data-driven recommendations and AI/ML solutions that enhance decision-making and deliver tangible value. Translate business needs - particularly within financial services domains such as marketing, risk, compliance and customer lifecycle management into well-defined machine learning problem statements and solution workflows. Solve business problems using analytics and machine learning techniques: Conduct exploratory data analysis, feature engineering, and model development to uncover insights and predict outcomes. Develop and deploy ML models, including supervised and unsupervised learning algorithms and model performance optimization. Design and implement scalable, cloud-native ML pipelines and APIs using tools like Python, Scikit-learn, Tensor Flow, and Py Torch. Collaborate with cross-functional teams to deliver robust and reliable solutions in cloud environments such as AWS, Azure, or GCP. Be a master storyteller for our services and solutions to our clients at various stages of engagement such as pre-sales, sales, and delivery using data-driven insights. Stay current with developments in AI, ML modelling, and data engineering best practices, and integrate them into project work. Mentor junior team members, provide guidance on modelling practices, and contribute to an environment of continuous learning and improvement. Job Requirements 4 to 7 years of relevant experience in building ML solutions, with a strong foundation in machine learning modelling and deployment. Strong exposure to banking, payments, fintech or Wealth/Asset management domains, with experience working on problems related to: Marketing analytics for product cross-sell/up-sell and campaign optimization Customer churn and retention analysis Credit risk assessment and scoring models Fraud detection and transaction risk modeling Customer segmentation for personalized targeting Experience in developing traditional ML models across business functions such as risk, marketing, customer segmentation, and forecasting. Bachelor’s or Master’s degree from a Tier 1 technical institute or MBA from Tier 1 institute Proficiency in Python and experience with AI/ML libraries such as Scikit-learn, Tensor Flow, Py Torch. Experience in end-to-end model development lifecycle: data preparation, feature engineering, model selection, validation, deployment, and monitoring. Eagerness to learn and familiarity with developments in Agentic AI space. Strong problem-solving capabilities and the ability to independently lead tasks or contribute within a team setting. Effective communication and presentation skills for internal and client-facing interactions. Ability to bridge technical solutions with business impact and drive value through data science initiatives.

Requirements

• Machine Learning
• Data Analysis
• Python
• AI/ML Libraries
• Cloud Environments

Qualifications

• Bachelor’s or Master’s degree from a Tier 1 technical institute or MBA from Tier 1 institute

Preferred Technologies

• Machine Learning
• Data Analysis
• Python
• AI/ML Libraries
• Cloud Environments

About the company

Sirius AI specializes in data-driven recommendations and AI/ML solutions aimed at enhancing decision-making in various sectors, particularly financial services.