Relevant academic background such as a diploma or degree in Telecommunications, Computer Science, Electrical/Electronic Engineering, or a related field.
Familiarity with common networking technologies and tools (e.g., TCP/IP, routing and switching, SNMP monitoring, ticketing systems).
Strong analytical and problem-solving abilities, attention to detail, and capability to work within defined procedures and service levels.
...
Act as Technical Product Manager in operations, ensuring all technical components (servers, middleware components, application modules, interfaces, job chains, schedulers, connectivity paths) remain secure, compliant and lifecycle‑current.
Monitor, track and plan technology lifecycle events (EoL/EoS, patch cycles, hardware refresh, OS upgrades, middleware version changes) together with infrastructure and platform teams, ensuring risks are identified early and scheduled into IBSol governance cycles.
Execute Incident, Problem, Change and Release Management according to IBSol service standards, including detailed analysis, task coordination, root‑cause identification and technical approvals.
...
Translate complex client requirements into scalable solution designs, including Proof of Concept (POC) verification and traditional IT-to-public cloud migrations.
Provide strategic technical support and advisory services to customers
Establish and improve governance frameworks, including delivery playbooks, industry compliance standards, and project post-mortem review processes
...
Assist with the implementation of a preventive maintenance program to ensure that building machinery and systems meet or exceed their rated life. Perform emergency repairs as needed.
Follow departmental policies for the safe storage, usage, and disposal of hazardous materials. Maintain a clean and safe workplace.
Review inpsection to building systems including fire alarms, HVAC, and plumbing to ensure operation of equipment is within design capabilities and achieves environmental conditions prescribed by client.
...
Identify opportunities to streamline business rules, policies, and operational processes to improve efficiency.
Drive harmonisation initiatives such as but not limited to billing systems, customer notifications, operational channels, and customer experience.
Analyse frontline, customer, and operational channel insights to identify gaps, pain points, and opportunities to enhance customer experience and operational effectiveness.
...
Quality Assurance & Compliance: Define performance metrics, continuous monitoring, health checks, periodic reviews, enforcement mechanisms, and automated validation rules to maintain 100% accurate dependencies, impact maps, and ticket routing logic
Process Enablement & Frameworks: Standardize playbooks, intake frameworks, and self-service guidelines to streamline how product teams interface with IT Service Management systems. Contextual layer for operational telemetry (dependencies, ownership, business mapping) to enable reasoning.
AI Tools and Automation Adoption: Define and adopt AI-driven Solutions and rulesets to support the identification of operational support structure gaps and the automation of the corrective actions in the CMDB. Provides the necessary high-quality, real-time data layer to safely automate operations and prevent AI "hallucinations “
...
Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
...