Technology Leader, Platform Architecture, Scalability & AI Automation

Role Overview

We are seeking a senior Technology Leader to own the architecture, scalability, and intelligent automation of our core FinTech platforms. This role is responsible for designing large-scale, cloud-native, distributed systems while leveraging AI-driven automation to improve platform efficiency, reliability, and operational scale.

The ideal candidate combines deep expertise in platform architecture and distributed systems with a strong point of view on using AI to automate infrastructure operations, optimize performance, and enable predictive, self-healing platforms. This is a highly technical leadership role with material influence over how the platform scales as the business grows.

Key Responsibilities

Platform Architecture & Technical Strategy

• Own the end-to-end platform architecture supporting core FinTech products and transaction flows
• Define architectural standards for scalability, performance, resiliency, and system composability
• Lead evolution from tightly coupled or monolithic systems toward distributed, service-oriented platforms
• Establish clear system boundaries, ownership models, and architectural governance
• Define and execute a multi-year platform roadmap aligned with growth, transaction scale, and product velocity

Scalability & Distributed Systems

• Design platforms capable of handling high transaction volumes, burst traffic, and sustained throughput
• Guide horizontal scaling strategies across compute, storage, data, and messaging layers
• Lead architectural decisions around sharding, partitioning, caching, asynchronous processing, and concurrency
• Continuously improve latency, throughput, and resource efficiency across the platform
• Enable multi-region and multi-environment scalability where required

Cloud & Infrastructure Architecture

• Architect cloud platforms (AWS, Azure, or GCP) optimized for scale, availability, and operational efficiency
• Define reference architectures for containerized workloads, microservices, and distributed runtimes
• Lead Kubernetes and container platform adoption and standardization
• Mature Infrastructure as Code (Terraform, CloudFormation, etc.) for consistent, scalable environments
• Own capacity modeling, growth forecasting, and infrastructure lifecycle planning

AI-Driven Automation & Intelligent Platforms

• Apply AI and machine learning techniques to automate platform operations and decision-making
• Use AI for:
• Capacity forecasting and demand prediction
• Anomaly detection in platform performance and system behavior
• Automated root-cause analysis and incident correlation
• Predictive scaling and infrastructure optimization
• Drive adoption of self-healing platform patterns where systems can respond automatically to failure or degradation
• Enable data pipelines, feature stores, and runtime environments required to support AI-enabled platform services
• Partner with data and engineering teams to productionize AI capabilities within core platform workflows

Platform Engineering & Developer Enablement

• Build shared platform capabilities that abstract complexity and enable product teams to scale independently
• Provide self-service infrastructure, golden paths, and opinionated platform tooling
• Standardize CI/CD, runtime environments, observability, and deployment patterns
• Reduce friction and cognitive load for application teams through strong platform design
• Measure and improve developer experience as a platform outcome

Reliability, Performance & Intelligent Operations

• Lead SRE practices focused on scalability, automation, and operational maturity
• Define and track SLIs/SLOs centered on throughput, latency, availability, and platform health
• Establish advanced observability (metrics, tracing, logging) as inputs to AI-driven insights
• Lead analysis of scaling failures, performance bottlenecks, and systemic inefficiencies
• Drive continuous improvement toward predictable, automated, and resilient operations

Required Qualifications

• 10 years of experience designing and operating large-scale distributed systems
• 5 years in senior technical leadership roles (Director, Principal, VP, or equivalent)
• Deep expertise in platform architecture, cloud-native design, and system scalability
• Strong hands-on experience with AWS, Azure, or GCP
• Proven experience with microservices, event-driven architectures, and distributed data systems
• Solid background in Infrastructure as Code and automation-first platform design
• Experience applying AI/ML concepts to operational or platform use cases

Preferred Qualifications

• Experience with high-volume transaction processing or real-time systems
• Strong Kubernetes and container platform experience
• Experience with event streaming platforms (Kafka or equivalent)
• Background modernizing legacy platforms at scale
• Experience with AI-assisted operations, AIOps, or intelligent monitoring platforms

Key Competencies

• Systems-level architectural thinking with a strong scalability mindset
• Ability to blend platform engineering and AI automation into practical solutions
• Technical credibility with senior engineers, architects, and leadership
• Pragmatic decision-maker who balances ideal architecture with real-world constraints
• Strong communicator who can translate technical strategy into business impact

30-60-90 Day Success Plan

First 30 Days – Understand & Assess

• Develop deep understanding of current platform architecture and scaling limits
• Review system topology, transaction paths, and performance characteristics
• Identify opportunities for automation, AI-driven optimization, and architectural simplification
• Build strong relationships across engineering, data, and product leadership

Days 31–60 – Architect & Automate

• Define target-state platform architecture with explicit scalability patterns
• Prioritize architectural improvements with the highest scale and automation leverage
• Introduce AI-enabled insights into observability, capacity, or incident analysis
• Establish platform standards, reference architectures, and design principles

Days 61–90 – Scale & Industrialize

• Deliver measurable improvements in throughput, latency, and platform stability
• Advance automation toward self-service, self-scaling, and self-healing capabilities
• Roll out platform-level AI automation for operations and performance optimization
• Finalize a multi-year platform and AI-automation roadmap
• Establish a culture of building intelligent systems designed to scale by default