Associate Applied AI Engineer – GenAI Systems

Required Qualifications:

• 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.

Preferred Qualifications:

• 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.
• When you join our team:
• 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.
• #LI-Hybrid
• The role being advertised is an existing vacancy.

About Manulife and John Hancock