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Tier-1 brand plus mid-level ML role in a metro increases applicant competition density and difficulty.
Core ML and cloud skills transfer across industries, but regulated finance and LLM specialization increase specificity.
Explicit 3+ years plus mandatory ML, cloud, IaC, and certification requirements create rigid filters.
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Design, develop, and operate scalable machine learning-driven products leveraging large language models (LLMs) within a regulated financial environment.
Collaborate with cross-functional teams including Data Science, Cybersecurity, and DevOps to deliver AI and cloud-based solutions and manage data processing systems.
Develop secure, high-quality production code, implement automated testing and AI-assisted software development tools to improve code quality and delivery speed.
3+ years of applied software engineering experience with formal training or certification in software engineering concepts.
Advanced Python programming skills and proven experience building and operating scalable ML-driven products.
Azure and/or AWS certifications (Architect, Big Data, AI/ML) with hands-on experience in Azure and AWS cloud technologies including Kubernetes and Airflow.
Experience working in a highly regulated environment and proficiency with full Software Development Life Cycle, Terraform, Infrastructure as Code, microservices performance tuning, and secure AI-assisted software development tool usage.
Experienced in designing and delivering large-scale cloud-native architectures and performance-optimized real-time applications.
Skilled in integrating emerging AI technologies and responsible AI practices within engineering workflows in a secure and compliant manner.
Ability to work effectively across technology and business teams in agile settings focused on financial services and AI-driven solutions.