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Strong Tier-1 brand, metro locations, and a visible ML Engineer role drive high candidate competition.
Strong MLOps skills transfer across industries but financial-services domain knowledge increases sensitivity.
Requires MLOps production experience, specific CI/CD tooling, GenAI expertise, and financial-services knowledge, raising shortlisting strictness.
Lead planning, design, and deployment of end-to-end machine learning model solutions in production environments, ensuring robustness, monitoring, and performance tuning.
Collaborate across teams to develop and maintain automated pipelines and improve system performance with a focus on fault finding and data distribution issues.
Engage with business stakeholders to align machine learning solutions with business strategy and deliver measurable outcomes using Agile methodology.
Academic background in a STEM discipline such as Mathematics, Physics, Engineering, or Computer Science.
Experience building, testing, deploying, and maintaining machine learning models in production using CI/CD tools like TeamCity and CodeDeploy.
Proficiency in Python programming with hands-on experience in traditional ML, Generative AI (GenAI), and Agentic AI applications.
Work Experience Required: Not explicitly mentioned in the JD
Operates effectively in multidisciplinary Agile data and analytics teams with leadership on complex ML projects.
Strong capability in coaching others, conducting AI tooling demos, and driving automation initiatives in responsible AI including QA regression and observability.
Domain knowledge of financial services to identify business impacts, risks, and opportunities linked to machine learning outputs.