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Tier-1 brand, popular backend role, metro location, and mid/senior level amplify applicant competition.
Strong backend and ML requirements are transferable, but enterprise finance context adds moderate domain bias.
Explicit 5+ years plus many mandatory technical, ML, and platform skills makes filters strict.
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Provide technical guidance and direction to business and technical teams, contractors, and vendors.
Develop secure, high-quality production code; review and debug code from others.
Drive adoption and governance of enterprise-approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes.
Formal training or certification on software engineering concepts with 5+ years applied experience.
Hands-on practical experience in Python, SQL, Databricks, and Knowledge Graphs in production.
Proficiency in at least one programming language such as Python, SQL, Java, or R.
Experience with platform tech stacks like AWS, Docker, Kubernetes, Databricks, and CI/CD pipelines.
Experienced in leading use of AI-assisted software development tools with ability to set team expectations for validating AI outputs securely and correctly.
Strong understanding of responsible AI usage, including data sensitivity, security, and resiliency in engineering workflows.
Skilled in machine learning techniques (especially NLP, Knowledge Graphs, LLMs) and deploying ML models on cloud platforms such as AWS using tools like Sagemaker and EKS.