Key Responsibilities:
- 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
Required Skills and Experience:
- 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
Preferred Qualifications:
- 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