Design and implement scalable machine learning solutions aligned with product and business objectives
Develop, test, and optimize ML models for deployment in production environments
Contribute to the development of Generative AI features such as conversational interfaces and personalization modules
Collaborate with product managers, data scientists, and engineers to integrate ML solutions into customer-facing products
Monitor performance of deployed models and iterate to improve accuracy, latency, and user experience
Follow best practices in model development, experimentation, and deployment
Participate in code reviews, technical discussions, and architectural design sessions
Translate business requirements into technical specifications in partnership with stakeholders
4–5 years of experience in applied machine learning and model deployment
Hands-on experience with Generative AI, NLP, or conversational AI technologies
Proficiency in Python and commonly used ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face)
Experience building and deploying models using cloud services (AWS, GCP, or Azure)
Strong grasp of ML pipelines, feature engineering, and basic MLOps practices
Ability to work collaboratively and communicate technical concepts clearly
Experience contributing to production-level ML solutions that deliver measurable value
Master’s degree in Computer Science, Machine Learning, or related discipline
Exposure to fintech or financial services domain is a plus
Familiarity with recommendation engines or personalization techniques
Contributions to open-source ML projects or community
Experience with model monitoring, CI/CD for ML, and versioning tools like MLflow or Kubeflow