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Medium: metro locations and visible ML role raise applicant density for senior positions.
High: specialized LLM/MLOps and model deployment skills limit cross-industry transferability.
High: explicit advanced ML, LLM, MLOps, deployment, and Python requirements create rigid technical filters.
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Design and engineer customer-centric, high-performance, secure, and robust software solutions with focus on automation, scalability, and reusability.
Define and develop architecture models and roadmaps for applications and software components to align with business and technical needs across products and domains.
Collaborate with feature teams to deliver high-quality software rapidly, oversee solution design and delivery approaches for platform software throughout its lifecycle.
Significant experience in software engineering, software/database design and architecture within DevOps and Agile environments.
Strong background in building and optimizing pipelines for LLMs, ML models, and data processing in AWS/GCP or OSS frameworks.
Proficiency in Python and expertise in ML Ops standards including CI/CD, model deployment, security, and performance tuning.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in advanced ML workflows including supervised/unsupervised learning, time-series forecasting, feature engineering, and retraining strategies.
Skilled in integrating capabilities across OpenAI/Anthropic models, GPU compute, AutoML, and feature stores with expertise in prompt engineering and API integration.
Effective in defining and enforcing engineering standards for scalable, observable, and secure ML and software operations in complex banking platform environments.