AI/ML Engineer & MLOps 80-100%

At Sunrise, we think bigger, go further and create new ideas. For us, working culture means achieving great things together, where respect and innovative ideas combine with real teamwork. At ADAO (AI, Data and Agentic Enablement Office), we bring people together to unlock the future of Sunrise through data, analytics, AI, and agentic technologies. We build strong data foundations, scale safe and reliable AI capabilities, and empower business units through a hub‑and‑spoke operating model to deliver meaningful results across the company.

• Design, build, deploy and operate production grade ML and GenAI solutions, including RAG components where applicable, delivering measurable business value for Sunrise.
• Ensure end-to-end lifecycle excellence from experimentation to automated deployment, monitoring, risk controls and continuous improvement in alignment with ADAO standards and governance.
• Translate business roadmaps into solution designs and implement robust training/inference pipelines and production services that meet all non‑functional requirements.
• Co-design solution architectures with Data Scientists and Domain Pods (batch/real-time, APIs, RAG patterns where relevant).
• Industrialize prototypes with automated tests, secure packaging, deployment/rollback, and performance/cost tuning.
• Build standardized CI/CD workflows, manage model promotion through dev→test→prod, and maintain monitoring and drift/retraining mechanisms.
• Maintain standard CI/CD/CT templates including model/prompt packaging, validation gates, and dev→test→prod promotion.
• Operate model registry/versioning and automated retraining triggers aligned to ADAO governance and CIO/IT change windows.
• Provide reproducible experimentation, manage feature store patterns and validation, and promote reusable datasets/features across Pods.
• Provide experiment tracking, dataset/feature versioning and feature-store patterns (offline/online parity).
• Run evaluation harnesses for ML & GenAI (regression, A/B tests, safety & multilingual checks) and share reusable assets.
• Produce compliant model documentation, implement required governance/security/privacy controls, and support GenAI safety and auditability measures.
• Produce model cards and compliance artefacts (data sources, limitations, privacy/bias controls) and evidence for audits.
• Implement Responsible AI / GenAI guardrails: grounding, HITL approvals, audit logs, and risk reviews with governance teams.
• Implement full observability for models and services, maintain runbooks and alerting, participate in incident resolution, and drive reliability improvements.
• Define SLOs/SLIs and implement monitoring/alerting for quality, drift, latency, availability and cost.
• Maintain runbooks, participate in on-call/incident postmortems, and drive corrective actions.
• Provide reusable ML/GenAI components, coach teams on MLOps/LLMOps best practices and ensure consistent engineering standards across Hub and Pods.
• Build reusable libraries and reference implementations (RAG connectors, evaluation utilities, deployment scaffolding).
• Coach Pod engineers through reviews, pairing and clinics; contribute to ADAO AI Academy materials.

• Key deliverables include: Standard ML/LLMOps pipeline template and reference architectures (batch/real-time/RAG), model registry & artefact versioning (models, prompts, datasets, features), monitoring dashboards with drift detection and SLO reporting, GenAI evaluation test suite (regression, safety, multilingual) and acceptance thresholds, model documentation pack and operational runbooks (audit-ready).