AI Machine Learning Engineer

2 weeks ago

Kollam, Kerala, India beBeeMachine Full time ₹ 1,20,00,000 - ₹ 1,50,00,000

Job Title

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  • Manage and develop machine learning solutions to enhance business decision-making.">
  • Translate business needs into well-defined machine learning problem statements and solution workflows.">
  • Solve complex business problems using analytics and machine learning techniques.">
  • Develop, deploy and optimize ML models using supervised and unsupervised learning algorithms.">
  • Design and implement scalable, cloud-native ML pipelines and APIs using Python, Scikit-learn, TensorFlow, and PyTorch.">
  • 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 clients at various stages of engagement 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.">
">Key Requirements">

 

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  • 4-7 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, customer churn and retention analysis, credit risk assessment and scoring models, fraud detection and transaction risk modeling, and customer segmentation.">
  • 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, TensorFlow, PyTorch.">
  • 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.">
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