Description

• Design agents on Google ADK: decide where LLM agents apply judgment and where workflow agents and deterministic tools run fixed checks. Define state, tools, callbacks and structured outputs for each review type.
• Own the pattern library: define reusable patterns (system-of-record cluster + validation type), shared connectors and components, and decide for each automation whether it’s a pattern build or a pattern instance.
• Check technical readiness: review the bank’s discovery packs (procedures, process markdown, SOR lists, document types, historical records) and send gaps back before build starts.
• Lead system-of-record integration: design data access across origination, servicing, core banking and document systems, including cross-system keys, lineage, query performance and handling of missing sources.
• Design document intelligence: build extraction and classification for large loan and account files using Gemini and Document AI, with page-level citations and confidence-based routing to human review.
• Define and measure accuracy: co-author per-agent acceptance cards (attribute-level accuracy, critical-exception recall, sample sizes, exclusions), and build evaluation harnesses with ADK evaluation and Vertex AI Gen AI evaluation.
• Lead production tuning: run root-cause analysis on failed cases (data, rules, extraction, workflow), prioritize fixes, and make sure every change is regression-tested against gold sets.
• Build in banking controls: least-privilege IAM, PII handling, versioned prompts and models under change control, audit-ready run records, and evidence for model-risk management reviews.
• Engineer for scale and cost: design for full-population monthly runs, covering cost per case, caching, latency, resilience to SOR outages, and observability through Cloud Logging and Monitoring.
• Use AI-assisted engineering: set guardrails for AI coding agents (e.g., Devin) and Gemini Code Assist, including pull-request-only changes, sandboxed data and human review.
• Lead and collaborate: run design reviews with onsite Tech Leads across pod pairs, mentor developers, and work directly with bank Product Owners, platform teams, SOR owners and model-risk stakeholders.

What you’ll bring (must-have)

• Agentic AI in production: hands-on experience designing and shipping LLM-based agents, not just prototypes, including evaluation, monitoring and iteration after go-live.
• Google ADK and Vertex AI: practical depth with ADK (LLM agents, workflow agents, tools, session state, callbacks, evaluation) and Gemini models on Vertex AI.
• Google Cloud architecture: Cloud Run, BigQuery, Cloud Storage, Pub/Sub, Secret Manager, IAM, VPC Service Controls, and Cloud Logging and Monitoring.
• Engineering: strong Python and advanced SQL against enterprise databases; API integration; structured outputs and schema validation; CI/CD and Git-based workflows.
• Evaluation discipline: gold datasets, attribute-level metrics, precision and recall trade-offs, LLM-as-judge calibration, and drift detection.
• Regulated industry experience: delivery in banking or financial services, ideally in compliance, quality assurance, lending operations, KYC/AML or audit, with working knowledge of model-risk governance (e.g., SR 11-7) and data-privacy controls.
• Design leadership: proven ability to set reusable architecture standards across multiple delivery teams and to defend design decisions with business stakeholders.

Nice to have

• Vertex AI Agent Engine for deploying and managing ADK agents; Document AI processors.
• Agent interoperability standards (e.g., MCP, A2A) and multi-agent orchestration.
• Mortgage or lending compliance knowledge (e.g., TRID disclosures and fee tolerances), or cards and payments disputes.
• Process-intelligence or task-mining tools for discovery.
• Certifications: Google Cloud Professional Cloud Architect, Professional Machine Learning Engineer

Key Skills

Education

Bachelor's degree