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).

Experience
5-9 yrs