Director of Artificial Intelligence

Job Title: VP-Digital Expert Support Lead

Experience

Location

Position Overview

Digital Expert Support Lead

Engineering, Product, AI/ML, SRE, DevOps, and Compliance teams

Role-Level Expectations

• Functionally accountable

  for all post-deployment support and performance assurance of digital expert systems.

• Operates at L3+ support level

  , enabling L1/L2 teams through proactive observability, automation, and runbook design.
• Leads

  stability engineering squads

  , AI support specialists, and DevOps collaborators across multiple business units.
• Acts as the

  bridge between operations and engineering

  , ensuring technical fixes feed into product backlog effectively.
• Supports continuous improvement through

  incident intelligence, root cause reporting, and architecture hardening

  .
• Sets the

  support governance framework

  (SLAs/OLAs, monitoring KPIs, downtime classification, recovery playbooks).

Position Responsibilities

Operational Leadership & Stability Engineering

1. Own the

   production health and lifecycle support

   of all digital expert systems across onboarding, payments, and cash management.
2. Build and govern the AI

   Support Control Center

   to track usage patterns, failure alerts, and escalation workflows.
3. Define and enforce SLAs/OLAs for LLMs, GenAI endpoints, NLP components, and associated microservices.
4. Establish and maintain

   observability stacks

   (Grafana, ELK, Prometheus, Datadog) integrated with model behavior.
5. Lead

   major incident response

   and drive cross-functional war rooms for critical recovery.
6. Ensure AI pipeline resilience through fallback logic, circuit breakers, and context caching.
7. Review and fine-tune inference flows, timeout parameters, latency thresholds, and token usage limits.

Engineering Collaboration & Enhancements

1. Drive

   code-level hotfixes

   or patches in coordination with Dev, QA, and Cloud Ops.
2. Implement automation scripts for diagnosis, log capture, reprocessing, and health validation.
3. Maintain

   well-structured GitOps pipelines

   for support-related patches, rollback plans, and enhancement sprints.
4. Coordinate enhancement requests based on operational analytics and feedback loops.
5. Champion enterprise integration and alignment with Core Banking, ERP, H2H, and transaction processing systems.

Governance, Planning & People Leadership

1. Build and mentor a high-caliber AI Support Squad – support engineers, SREs, and automation leads.
2. Define and publish

   support KPIs

   , operational dashboards, and quarterly stability scorecards.
3. Present production health reports to business, engineering, and executive leadership.
4. Define runbooks, response playbooks, knowledge base entries, and onboarding plans for newer AI support use cases.
5. Manage relationships with AI platform vendors, cloud ops partners, and application owners.

Must-Have Skills & Experience

• 12+ years

  of software engineering, platform reliability, or AI systems management experience.
• Proven track record of leading support and platform operations for

  AI/ML/GenAI-powered systems

  .
• Strong experience with

  cloud-native platforms

  (Azure/AWS),

  Kubernetes

  , and

  containerized observability

  .
• Deep expertise in

  Python and/or Java

  for production debugging and script/tooling development.
• Proficient in

  monitoring, logging, tracing, and alerts

  using enterprise tools (Grafana, ELK, Datadog).
• Familiarity with

  token economics

  , prompt tuning, inference throttling, and GenAI usage policies.
• Experience working with

  distributed systems, banking APIs, and integration with Core/ERP systems

  .
• Strong understanding of

  incident management frameworks (ITIL)

  and ability to drive

  postmortem discipline

  .
• Excellent stakeholder management, cross-functional coordination, and communication skills.
• Demonstrated ability to mentor senior ICs and influence product and platform priorities.

Nice-to-Haves

• Exposure to enterprise AI platforms like OpenAI, Azure OpenAI, Anthropic, or Cohere.
• Experience supporting

  multi-tenant AI applications

  with business-driven SLAs.
• Hands-on experience integrating with compliance and risk monitoring platforms.
• Familiarity with

  automated root cause inference

  or anomaly detection tooling.
• Past participation in enterprise architecture councils or platform reliability forums