Associate Applied AI Engineer – GenAI Systems

• Translate business problems into a clear solution approach, including user workflow, data flow, model approach, evaluation plan, and operational controls.
• Create lightweight, high-quality design artifacts such as system context, runtime sequence, agent/tool maps, data lineage, and decision logs that support implementation and governance.
• Participate in design discussions and make thoughtful trade-offs across accuracy, explainability, cost, latency, and maintainability.

• Develop ML solutions such as forecasting, classification, NLP, anomaly detection, optimization, and scenario analysis.
• Build GenAI capabilities such as retrieval-based solutions (RAG), structured summarization and extraction, transaction understanding, variance explanation, and tool-using workflows where appropriate.
• Engineer features from structured and unstructured data and help ensure solutions remain robust as data evolves.

• Implement performance evaluation using holdouts, backtesting, error analysis, and fit-for-purpose metrics aligned to the business problem.
• For GenAI, help design practical evaluation approaches such as scenario coverage, edge cases, human review rubrics, quality scoring, and regression testing.
• Document model limitations clearly and support guardrails that improve the reliability and safe use of outputs.

• Collaborate with Data Engineering, ML Engineering, and Software teams to productionize solutions through reliable data pipelines, model packaging, CI/CD, deployment, and monitoring.
• Write maintainable, tested code using strong software engineering practices such as version control, modular design, logging, and code review.
• Support monitoring for data quality, drift, performance deterioration, and operational failures, and help investigate issues when thresholds are breached.
• Contribute to runbooks and support adoption and UAT with business users.

• Contribute to the documentation and evidence required for model risk review, including assumptions, validation results, monitoring plans, UAT evidence, and approvals.
• Ensure privacy and security expectations are met through data minimization, appropriate access controls, and safe handling of sensitive information.
• Follow established standards for reproducibility, traceability, model documentation, and auditability.

• Learn quickly from design reviews, code reviews, and stakeholder feedback, and apply those lessons to future work.
• Contribute reusable components, templates, examples, and testing patterns that make team delivery faster and more consistent.
• Stay current with emerging AI and GenAI engineering patterns and bring forward practical ideas that improve how the team builds solutions.

• Master’s or PhD in Computer Science, Statistics, Machine Learning, Applied Mathematics, Operations Research, Engineering, or a related quantitative field.
• 0–3 years of experience in applied data science / machine learning, including internships, co-ops, research, or early-career industry experience; strong academic project work may also be considered.
• Strong Python and SQL, with solid software engineering fundamentals such as Git-based workflows, code reviews, unit and integration testing, logging, readable code structure, debugging, and basic performance tuning.
• Hands-on experience with modern DS/ML tooling such as scikit-learn, PyTorch/TensorFlow, Spark/Databricks or similar, including data preparation, feature engineering, and model development.
• Demonstrated ability to turn a problem into a structured technical approach, including clear thinking around inputs, outputs, assumptions, failure modes, and evaluation.
• Exposure to building or evaluating GenAI solutions, including at least one of: RAG, structured summarization/extraction, LLM-based classification, tool/function calling, or multi-step workflows.
• Strong evaluation mindset across ML and GenAI, including metric selection, holdout testing, error analysis, scenario coverage, edge-case thinking, and basic regression testing approaches.
• Understanding of production-oriented development, including packaging code, working with APIs or services, handling configuration, monitoring outputs, and designing for maintainability.
• Strong communication skills, with the ability to explain technical outputs, limitations, and design choices in plain language.

• Graduate-level training with applied research or project experience in AI/ML, demonstrated through thesis work, capstone projects, publications, internships, co-ops, open-source contributions, or industry collaboration.
• Hands-on GenAI experience across multiple patterns such as RAG, prompt orchestration, structured outputs, tool/function calling, and agentic workflows.
• Familiarity with GenAI system components such as vector databases, embeddings, semantic search, reranking, orchestration frameworks, and prompt/version management.
• Experience with cloud-based data and ML environments such as Azure, Databricks, MLflow, model registries, CI/CD pipelines, and API deployment patterns.
• Experience building software beyond notebooks, including libraries, services, reusable modules, or internal tools used by others.
• Familiarity with evaluation frameworks for GenAI, including test sets, rubric-based review, regression packs, and quality monitoring.
• Exposure to Finance, Treasury, Insurance, IFRS-17, or Actuarial use cases, and/or familiarity with model governance practices such as documentation, validation evidence, and monitoring plans.
• Experience implementing practical GenAI guardrails, such as hallucination reduction, grounding strategies, safe output formatting, access controls, and human review workflows.
• Evidence of technical initiative through publications, open-source contributions, hackathons, strong capstone projects, or applied research with real-world impact.

• We’ll empower you to learn and grow the career you want.
• We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.
• As part of our global team, we’ll support you in shaping the future you want to see.

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