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Tier-1 employer, mid-level AI title, metro location, broad skillset and 3–5 years amplifies competition.
Core AI engineering skills are transferable, but finance and regulated-domain experience increases fit sensitivity.
Explicit 3–5 years, mandatory AI/production engineering skills and software fundamentals increase filter strictness.
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Design, develop, experiment with, and deploy AI/ML and Generative AI solutions (e.g. LLM, prompt engineering, RAG) to improve Finance Technology applications.
Apply software engineering best practices including CI/CD, testing, and monitoring to ensure reliability and continuous improvement of AI-enabled financial systems.
Collaborate across finance, data, and engineering teams to create reusable AI adoption frameworks, standards, and support responsible AI use within Finance Technology.
3–5 years' experience in AI Engineering, Software Engineering, Data Engineering, ML Engineering, or related technical discipline.
Proficient in Python and SQL with hands-on experience in AI/ML or Generative AI development and deployment.
Strong software engineering skills including Git, CI/CD, automated testing, and code quality.
Work Experience Required: 3–5 years in relevant tech roles.
Experienced in delivering AI/ML solutions within production environments, from prototype to deployment, with a focus on reliability and maintainability.
Comfortable working at the intersection of AI and software engineering, driving practical AI adoption in finance technology contexts.
Familiarity or experience with enterprise AI/data platforms and finance-related technologies (e.g. Databricks, Oracle Fusion) is advantageous but not mandatory.