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Tech Lead - Al Solutions

Orysys Limited   Colombo • Full-time

The Tech Lead - Al Solutions will serve as the crucial integration bridge between the Bank's core systems, digital channels, and advanced Al models. This role focuses on Application-Centric Al Engineering. You will treat Al models as external services and engineer the API wrappers, event-driven architectures, and failover mechanisms necessary to securely wire these models into our on-premise legacy systems and modern Customer Data Platforms. You will oversee the technical integration of vendor-delivered solutions into our legacy and modern applications.

Key Responsibilities:

  • Lead Solution Architecture, Integration & Al Engineering: Architect scalable API layers and event-driven patterns to securely connect Al models and CDP systems with the Bank's on-premise legacy systems.
  • Stakeholder Communication: Act as a bridge between technical teams and business/product stakeholders, translating business requirements into robust technical integration solutions.
  • Project Management & Delivery: Manage timelines, estimate tasks, and coordinate with vendors to ensure smooth productionization of Al use cases.
  • Failover & Security Design: Ensure that introducing Al into customer journeys does not introduce unacceptable system lag, designing proper fallback mechanisms.

Qualifications:

  • Bachelor's degree in Computer Science, IT, or a related discipline.
  • 5+ years in a hands-on software engineering or integration role, including 1+ years of Al engineering.
  • Previous roles: Tech Lead, Senior Al Engineer, or Integration Lead.
  • Project experience in large-scale applications, legacy system support, and IT modernization within an enterprise banking environment.

Technical Skills:

  • Programming: Java/Spring Boot, Python
  • Integration: REST/SOAP APIs, API Gateways (Apigee, Kong), Message Brokers (Kafka, RabbitMQ)
  • Cloud Platforms & DevOps: Cloud Run, Kubernetes, Docker, Terraform, AWS/GCP
  • Al & DS Technical Architect Skills: Understanding of how to consume ML models efficiently in production. Understanding of database systems.