Senior Application Development Lead-Senior Vice President

Architecture:
Microservices, domain-driven design, event-driven systems

As the App Dev Senior Tech Lead Analyst, you will be responsible for the technical vision, architecture, and execution of this greenfield platform. You will lead a world‑class engineering team from the ground up, while remaining deeply technical and contributing to the core development of the platform. This is a unique opportunity to blend strategic leadership with hands‑on engineering to build a product that will have a direct and measurable impact on the front lines of our business.

• Platform Architecture & Development:
  Lead the design, architecture, and hands‑on development of a scalable, secure, and resilient agentic AI platform from concept to production.
• Technical Leadership & Hands‑On Engineering:
  Serve as the lead engineer and technical authority, guiding critical decisions on frameworks, technologies, and infrastructure. You will be expected to write code, build prototypes, and lead by example.
• Strategic Road mapping:
  Partner closely with product management and senior business leaders in banking to define the product strategy and technical roadmap. Translate complex business needs into elegant technical solutions.
• Cross‑Functional
  
  Collaboration:
  
  Partner effectively with horizontal AI platform teams, enterprise architecture, and external vendor partners to leverage existing capabilities, influence roadmaps, and accelerate delivery.
• AI & ML Integration:
  Drive the strategy for integrating and operationalizing Large Language Models (LLMs), agentic frameworks (e.g., Google ADK, Lang Chain), and other AI/ML technologies to solve real‑world banking challenges.
• Operational Excellence:
  Implement and champion best‑in‑class engineering practices, including CI/CD, automated testing, infrastructure‑as‑code, and robust monitoring to ensure enterprise‑grade reliability.
• Business Impact:
  Define, measure, and report on key performance indicators (KPIs) related to platform adoption, user productivity, and the ultimate impact on business outcomes like wallet share growth.
• Compliance & Security:
  Ensure the platform adheres to the highest standards of data privacy, security, and regulatory compliance required in the banking industry.

• Education:
  
  Bachelor's or Master’s degree in Computer Science, Engineering, or a related technical field.
• Experience:
  
  10+ years of experience in software engineering, with at least 4+ years in a leadership role leading high‑performing engineering teams.
• Hands‑On Leader:
  Proven experience as a "player‑coach" who can lead from the front, contribute to the codebase, and mentor junior and senior engineers.
• Platform Building: A strong track record of designing, building, and launching scalable, distributed, cloud‑native platforms from the ground up.
• Domain Knowledge:
  Experience in the financial services industry (Corporate Banking, Investment Banking, Fin Tech) is a significant plus. An understanding of banking workflows and data is highly desirable.
• Communication
  
  Skills:
  
  Exceptional ability to communicate complex technical concepts to non‑technical stakeholders and to articulate a clear technical vision that aligns with business goals.

• AI / GenAI /Agentic AI
  • LLM & Agentic Frameworks:
    Deep expertise in building production‑grade agentic systems using GCP as primary (ADK, Vertex AI)
  • Multi‑Agent Systems:
    Hands‑on experience designing and implementing multi‑agent architectures (task decomposition, coordination, orchestration, and agent‑to‑agent interaction patterns)
  • Model Context Protocol (MCP) & Integrations:
    Experience integrating agents with enterprise tools and data sources using MCP or equivalent context‑sharing patterns
  • Knowledge Graphs & Reasoning:
    Building and leveraging knowledge graphs for context enrichment, reasoning, and workflow automation
  • RAG & Knowledge Systems:
    End‑to‑end RAG pipelines using enterprise search + vector stores (e.g., Elastic, Pinecone) with grounding, evaluation, and optimization
  • Model Lifecycle & Governance:
    Model evaluation, monitoring, prompt/version control, and Responsible AI / MRM compliance
• Enterprise AI Integration & Data
  • API‑and…