Machine Learning Specialist

Machine Learning Specialist Attributes & Experience

Core Attributes

• Systems thinker with strong product judgement
  Designs ML capabilities as part of end to end workflows that support human decision making, rather than isolated models or autonomous automation.
• Governance first mindset
  Naturally designs for explainability, auditability, confidence scoring, and human in the loop review, especially in regulated or risk sensitive environments.
• Global scale, configuration led thinker
  Assumes multi tenant, multi region, and multi language operation by default; treats variation (language, locale, market context) as configuration and metadata rather than bespoke logic.
• Comfortable with ambiguity and evolving standards
  Thrives in environments where schemas, policies, and operating models are still being shaped, and can make progress without waiting for perfect definitions.
• Collaborative and cross functional
  Works closely with Product, Architecture, Security, and Engineering to translate business and operational problems into robust ML enabled solutions.

Required Experience

• Designing and delivering agentic or LLM enabled systems in production
  Experience building multi step, tool using AI workflows (e.g. classification, analysis, summarisation, drafting) that operate with bounded autonomy and clear escalation paths.
• Reasoning over structured and semi structured data
  Proven ability to work with schemas, metadata, versions, and diffs using ML to support interpretation, classification, and impact analysis rather than raw prediction.
• Production grade ML engineering
  Experience deploying, monitoring, and evolving ML systems with attention to reliability, versioning (models, prompts, policies), observability, and safe failure modes.
• Designing ML systems for enterprise security constraints
  Familiarity with access controlled environments (e.g. RBAC / ABAC concepts), and experience ensuring ML outputs respect tenant, product, region, and data visibility boundaries.
• Building globally reusable ML capabilities
  Demonstrated experience designing ML or AI features that scale across markets and languages without duplication, re architecture, or market specific forks.

Nice to Have (Signals of Strong Fit)

• Experience combining deterministic rules with ML/LLMs to improve consistency, safety, and trust.
• Experience working in regulated industries (insurance, financial services, healthcare, etc.).
• Familiarity with schema driven or metadata driven platforms.
• Experience designing ML systems that explicitly support governance, review, and compliance workflows.

Explicit Non Goals of the Role (to set expectations)

• This is not a pure research or academic data science role.
• This role does not focus on fully autonomous AI decision making.
• Linguistic expertise is not required; the emphasis is on global scale system design, not translation quality.

One Line Summary (Optional)

An applied ML specialist who builds globally scalable, agent enabled systems that augment human judgement, operate safely within enterprise access controls, and are designed for governance, reuse, and long term platform evolution.