Sr. Engineer - IT Machine Learning

Responsibilities:

• Writes, tests, and documents technical work products (e. g., code, scripts, processes) according to organisational standards and practices
• Devotes time to raising the quality and craftsmanship of products and systems.
• Conducts root cause analysis to identify domain-level problems and prescribes action items to mitigate.
• Designs self-contained systems within a team's domain, and leads implementations of significant capabilities in existing systems.
• Coaches team members in the execution of techniques to improve reliability, resiliency, security, and performance.
• Decomposes intricate and interconnected designs into implementations that can be effectively built and maintained by less experienced engineers.
• Anticipates trouble areas in systems under development and guides the team in instrumentation practices to ensure observability and supportability.
• Defines test suites and instrumentation that ensures targets for latency and availability are being consistently met in production.
• Leads through example by prioritising the closure of open vulnerabilities.
• Evaluates potential attack surfaces in systems under development, identifies best practices to mitigate, and guides teams in their implementation.
• Leads team in the identification of small batches of work to delivery the highest value quickly
• Ensures reuse is a first-class consideration in all team implementations and is a passionate advocate for broad reusability
• Formally mentors teammates and helps guide them to and along needed learning journeys.
• Observes their environment and identifies opportunities for introducing new approaches to problems.

Requirements:

• Bachelor's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training
• 3+ years of experience designing, developing, and implementing large-scale solutions in production environments.
• Master's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training
• Airline Industry experience.
• Ability to effectively communicate both verbally and in writing with all levels within the organisation.

Proficiency and demonstrated experience in the following technologies/frameworks:

Programming and Backend Development:

• Python (backend services/APIs, automation, testing).
• SQL (advanced querying, window functions, optimisation, data modelling fundamentals).
• REST APIs, JSON, service integration patterns.

AI Agents / Agent Frameworks (Must-have):

• Hands-on experience with at least one enterprise-grade agent framework such as:
• LangGraph/Crew AI/ Autogen (or equivalent agent orchestration frameworks).
• Copilot Studio agents /Open AI agents (or equivalent).
• Agent orchestration, tool/function calling, structured outputs, evaluation and iteration patterns.

Analytics, Reporting and Dashboards:

• Power BI and/or Tableau.
• Metrics definition, reporting automation, executive-ready dashboards.

Cloud Platforms:

• Microsoft Azure or AWS.
• Experience deploying data/AI workloads in cloud environments.

Data and Integration:

• Data pipelines/ELT concepts, dataset curation for analytics.
• Integration with an enterprise risk assessment and governance workflow platform (tool-agnostic).

AI/ML Fundamentals:

• Applied understanding of ML lifecycle concepts
• Familiarity with LLM concepts like prompting, context handling, grounding patterns like RAG, Evals, Agent Behaviour Monitoring, etc.

Governance, Risk and Regulatory Knowledge:

• Working awareness of AI/data regulations and governance expectations such as GDPR, EU AI Act, and similar privacy/AI governance frameworks.
• Understanding of governance controls: evidence capture, auditability, traceability, Observability, exception management, and reporting.

Software Engineering Practices:

• Object-oriented design and clean coding principles.
• Version control (Git) and CI/CD fundamentals.
• Testing practices (unit/integration), documentation, logging/monitoring, operational readiness.

Licenses and Certifications (Preferred, not required):

• Microsoft Certified: Azure Data Engineer Associate / Azure Developer Associate / Azure AI Engineer Associate.
• AWS Certified Developer - Associate / AWS Certified Data Engineer - Associate (or equivalent AWS certifications).
• Power BI or Tableau certification (if applicable/available).
• Any relevant certifications in data privacy or AI governance/risk (e. g., privacy fundamentals, responsible AI).

Methodologies and Tools (Preferred):

• Experience working in Agile (Scrum/Kanban) environments.
• Familiarity with DevOps toolchains (e. g., CI pipelines, code quality checks, artefact repos).