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Tier-1 employer, popular backend title, mid-level requirement, and broad AI/Databricks skillset increase applicant competition.
Strong backend and ML production requirements make skills transferable across industries despite optional finance knowledge.
Multiple mandatory technical filters (Python, Databricks, NLP/LLMs, cloud) and 5+ years increase screening strictness.
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Provide technical leadership and guidance to business and technical teams, contractors, and vendors.
Develop, review, and debug secure, high-quality production code focusing on Python, SQL, Databricks, Knowledge Graphs, and multiple programming languages.
Drive adoption and governance of AI-assisted engineering practices to enhance code quality, delivery speed, and operational outcomes, including setting validation standards and promoting reuse within the SDLC/TLM toolchain.
Formal training or certification in software engineering concepts with 5+ years of applied experience.
Proficient hands-on experience in Python, SQL, Databricks, Knowledge Graphs in production and advanced in at least one programming language including Python, Java, or R.
Practical experience leading use of enterprise-authorized AI-assisted software development tools and strong understanding of responsible AI use including security and data sensitivity.
Work Experience Required: 5+ years in software engineering roles with demonstrated leadership in AI-assisted development environments.
Experienced in cloud-native platforms such as AWS, Docker, Kubernetes, Databricks, and CI/CD pipelines with strong ML and NLP background including Knowledge Graphs and Large Language Models.
Comfortable influencing product design, technical operations, and software development lifecycle processes, and serving as a subject matter expert and advocate within the engineering community.
Skilled in optimizing and deploying ML models on cloud platforms using tools like AWS Sagemaker and EKS, with knowledge of financial services IT systems and data engineering practices for AI model training.