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Tier-1 brand and seniority make competition moderate despite specialized ML-cloud skill requirements.
Strong ML and cloud skills transferable, but financial risk product context increases domain specificity.
Explicit 9–12 years plus many mandatory ML, cloud, and devops tech stack requirements.
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Design, build, and maintain efficient, reusable code based on user requirements in Python, with a focus on ML projects, cloud-native architecture, and microservices.
Lead and coordinate a team of engineers through all phases of software development using agile methodologies, ensuring high software quality and adherence to company standards.
Collaborate closely with QA, DevOps, and other teams to support software deployment, automated testing, and problem resolution workflows.
9 to 12 years of hands-on software engineering experience with expert knowledge in Python, Airflow, Machine Learning, and cloud-native architectures.
Proficiency in SOA, REST services, microservices, AWS deployment using Terraform/CloudFormation, containerization (Docker, EKS, Kubernetes), and GitLab CI/CD.
Experience leading teams through software development lifecycle including design patterns, secure coding, and test-driven/behavior-driven development.
Work Experience Required: 9 to 12 years; Educational degree desirable but not mandatory; Notice period: Not explicitly mentioned in the JD.
Experienced technical leader with demonstrated ability to lead ML projects end-to-end, including data ingestion and model training.
Strong background in agile development environments with emphasis on software quality, automated testing, and continuous integration/delivery.
Familiarity with cloud infrastructure, containerized deployments, API interfaces, and security best practices in software engineering.