Back
Mid-level, metro ML role with common skill set increases candidate competition.
Core ML engineering skills transfer across industries, though enterprise/BFSI domain experience is preferred.
Explicit 5+ years plus mandatory ML frameworks, MLOps, and cloud skills increase filtering strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy machine learning and AI solutions for enterprise-grade applications in banking and financial services.
Manage end-to-end ML model lifecycle including data exploration, feature engineering, training, validation, deployment, and monitoring using MLOps best practices.
Collaborate with cross-functional teams including DevOps, product owners, and stakeholders; mentor junior AI/ML developers and maintain technical documentation.
5+ years of industry experience in AI/ML development.
Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or related field (or equivalent industry experience).
Strong programming skills in Python plus familiarity with Java, Scala, or R, and experience with ML/DL frameworks such as TensorFlow, Keras, PyTorch, or scikit-learn.
Experience with cloud platforms (AWS/Azure/GCP), MLOps tools (e.g., MLflow, Kubeflow), and standard software engineering practices including CI/CD.
Experienced AI/ML developer comfortable with production-grade model deployment and operationalization using MLOps.
Deep understanding of ML/DL algorithms and hands-on experience with both data engineering and AI solution architecture in enterprise environments (preferably BFSI).
Able to translate business needs into scalable AI/ML solutions and communicate technical insights effectively to both technical and non-technical stakeholders.