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Tier-1 employer, metro locations, and mid-level AI role attract strong applicant competition.
Core LLM, OCR, and production ML skills are transferable, though bank operations domain adds some specificity.
Mandatory five-year AI experience and specific LLM, OCR, and production engineering skills enforce strict filters.
Design, build, deploy, and operate production-grade AI assistants automating high-volume knowledge work with a focus on intelligent document processing.
Define success metrics, perform offline evaluation and production monitoring to improve AI solution quality, reliability, and accuracy.
Embed Responsible AI controls, troubleshoot full solution stack issues, and produce health reports on adoption, accuracy, and exception rates.
At least 5 years of hands-on experience in applied AI, machine learning, or AI-powered automation with production or production-like delivery.
Strong proficiency in Python (including asynchronous programming), and experience with AI techniques such as prompt engineering and schema validation using Pydantic.
Experience with Optical Character Recognition (OCR) and document digitization workflows, including tools like AWS Textract or equivalent.
Work Experience Required: Minimum 5 years in relevant AI/ML automation domain.
Experienced in developing multi-stage AI workflows using graph-based orchestration frameworks (e.g., LangGraph) and integrating external tools with strong validation and error handling.
Skilled at defining objective success metrics, performing root-cause analysis on adoption and performance, and communicating across technical and non-technical stakeholders.
Familiar with production readiness practices including testing, CI/CD, secure development, logging, metrics collection, and incident response for enterprise-grade AI deployments.