Minimum qualifications:

• Bachelor's degree in Computer Science, a related field, or equivalent practical experience.
• 7 years of experience in software or data engineering, including one or more programming languages (e.g., Python, Go, Java), and with design patterns, testing frameworks, and API contract design.
• Experience using machine learning methodologies (deep learning, reinforced learning), model identification, selection and AI operations (e.g., model monitoring).
• Experience using Generative AI and agentic orchestration utilizing frameworks (e.g., LangChain, CrewAI, or Vertex AI Agent Builder) and vector databases.

Preferred qualifications:

• Experience in financial services, and with the regulatory and operational clearing, settlement, or custody.
• Experience with FSI regulatory practices and data residency, encryption at rest/transit (CMEK), and "explainable AI" requirements in banking.
• Experience with data modeling of relational, NoSQL, and analytical data modeling (Star Schema, Data Vault, etc.).
• Experience in BigQuery, Vertex AI, Dataflow, and Pub/Sub with an ability to drive the discovery phase, moving from a vague business problem to a structured product requirement document (PRD) and a working technical demo.
• Experience working in a high-maturity DevOps culture (e.g., trunk-based development, automated testing, blue/green deployments).

About the job

Responsibilities

• Design and build autonomous agentic workflows utilizing machine learning and generative AI technologies as part of a fully autonomous or human-in-the-loop agentic workflow.
• Partner with client leads(business user) to identify high-impact AI use cases. Translate these into product requirement documents (PRDs), clearly defining critical user journeys (CUJs) and success metrics.
• Evaluate and integrate AI solutions with modern data foundations, including relational databases, data lake houses, and real-time streaming architectures.
• Ensure all prototypes are built with a "production-first" mindset. Implement basic CI/CD pipelines and utilize infrastructure-as-code (IaC) (e.g., Terraform) to ensure environments are reproducible and secure.
• Create clear technical guides to ensure a seamless hand-off from POC to engineering teams.