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Tier-1 brand, metro location, and broad Python/ML backend requirements increase candidate competition.
Core Python, ML, and cloud skills are transferable, though financial risk domain experience increases specificity.
Explicit 9-12 years requirement plus mandatory ML, AWS, Terraform, and Kubernetes skills enforce strict filters.
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Own design, development, and maintenance of Python-based software systems for risk products including Connected Risk and World Check.
Lead and coordinate a team of engineers using agile methodologies to deliver high-quality software and ensure effective testing and deployment.
Collaborate with QA and DevOps teams to ensure software quality, automated testing, containerized deployments, and operational support.
9 to 12 years of hands-on software engineering experience with strong expertise in Python, Airflow, Machine Learning, and cloud native architectures (AWS).
Proven experience leading ML projects end-to-end including data ingestion and model training.
Strong experience with microservices, REST APIs, AI tooling (including LLM frameworks), containerization (Docker, Kubernetes), Infrastructure as Code (Terraform/CloudFormation).
Experience with SQL databases, software development lifecycle including Agile, TDD/BDD, and secure coding practices.
Experienced technical lead capable of managing and mentoring a software development team within a formal agile environment, delivering robust, scalable ML-enabled cloud applications.
Broad knowledge of cloud technologies and evolving AI/ML tools integrated into software engineering and deployment pipelines.
Comfortable working with complex financial risk products and collaborating across technical and business teams to drive product development and operational excellence.