Application Development Technology Senior Lead- Senior Vice President

As the App Dev Senior Tech Lead Analyst, you will be responsible for the technical vision, architecture, and execution of Financial Analytics applications. You will lead a world‑class technology team, 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. 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 and scalable 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 such as 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:
  
  12+ years of software engineering experience, with at least 2+ 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:
  Strong track record of designing, building, and launching scalable, distributed, cloud‑native platforms from the ground up.
• Domain Knowledge:
  Experience in the financial services, especially Financial Analytics, 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.

• Backend & Distributed Systems
  • Languages:
    
    Java full stack/Spring Boot (enterprise standard) and Python Go (plus)
  • Architecture:
    Microservices, domain‑driven design, event‑driven systems
  • APIs & Integration: REST/gRPC;
    Apigee, Kong
  • Data & Messaging:
    PostgreSQL/Oracle, MongoDB
• AI / GenAI
  • LLM & Agentic Frameworks:
    Deep expertise in building production‑grade agentic systems using GCP as primary (ADK, Vertex AI)
  • 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 event‑driven integration of AI into enterprise workflows
  • Dat…