Senior Engineer AI

• Collaborate with cross-functional teams to understand business requirements identify opportunities for AI solutions and integrate models into enterprise processes and applications;

• Lead the design development deployment and maintenance of AI models using Watsonx and other enterprise AI platforms;

• Build and maintain generative AI applications automation pipelines and traditional machine learning solutions;

• Own the end-to-end AI lifecycle including data preparation feature engineering model training evaluation deployment monitoring and retraining;

• Implement scalable MLOps pipelines and production-grade AI solutions to ensure performance reliability and scalability;

• Determine appropriate machine learning techniques and algorithms for models evaluate model performance using relevant metrics and leverage frameworks such as Scikit-learn XGBoost TensorFlow Keras or PyTorch for model development;

• Ensure AI model governance compliance and ethical AI practices across the AI lifecycle;

• Integrate AI services with enterprise applications including use of Model Context Protocol (MCP) where ;

• Integrate agents with tools APIs and knowledge bases to enable dynamic decision-making and task automation;

• Develop and expose RESTful APIs and/or gRPC services for AI model consumption;

• Work with data governance and data modeling teams to source and manage model data and processes;

• Develop and maintain codebase for models using AIOps/MLOps tools (e.g. Watsonx GitHub) ensuring performance reliability and scalability;

• Develop and maintain model documentation including architecture parameters hyperparameters and lifecycle artifacts;

• Create and maintain dashboards reports and visualizations to communicate model performance business impact and compliance outcomes;

• Produce governance documentation to support audits regulatory reviews and stakeholder transparency.

2) Prompt Engineering & AI Agent Development

• Design and implement prompt engineering strategies for large language models (LLMs) and AI agents;

• Apply retrieval-augmented generation (RAG) techniques to enhance model grounding and response quality;

• Align prompt workflows with solution objectives and evaluate effectiveness.

3) Exploratory Data Analysis & Feature Engineering

• Conduct exploratory data analysis (EDA) to identify relationships patterns and trends in data;

• Identify and address data quality issues that may impact model performance;

• Use statistical techniques to select significant variables and features for model development;

• Proficiency in vector databases () and graph databases (e.g. Neo4j);

• Develop data visualizations to communicate findings and insights effectively to stakeholders.

4) Stakeholder Collaboration & Technical Leadership

• Collaborate with business data and engineering teams to understand requirements and translate AI capabilities into actionable business solutions;

• Work with stakeholders to define success criteria and integrate models into business processes and applications;

• Mentor and coach junior engineers fostering a culture of technical excellence innovation and continuous learning;

• Lead AI projects and initiatives ensuring alignment with timelines quality standards and business impact;

• Track and report progress using tools such as Jira ensuring visibility and accountability across teams;

• Clearly document task requirements completed work and model integration steps to support transparency and reproducibility;

• Stay current with AI trends tools and best practices and apply relevant innovations to enterprise AI solutions.

QUALIFICATIONS

• University Degree - Computer Science / Engineering or Mathematics;

• Cloud AI Platform Developer or Architect Certification;

• Watson Studio or Watson X Certification;

• At least 8 years of experience in developing Data Science and AI applications;

• Minimum8 yearsof hands-on experience in Data Science or AI/ML roles including generative AI automation and traditional machine learning;

• Proven success in designing developing and deploying AI models in production environments using frameworks such as Scikit-learn XGBoost TensorFlow Keras or PyTorch;

• Experience working with large-scale datasets data mining data modeling and enterprise-grade data pipelines;

• Strong programming skills in Python R or Java with proficiency in SQL and data integration concepts including ETL;

• Hands-on experience with enterprise AI platforms such as WatsonX AWS SageMaker Azure ML or IBM Cloud Pak for Data;

• Familiarity with cloud services including AWS (S3 Redshift Glue EMR) Azure (Blob Storage Data Factory) and IBM CP4D;

• Deep understanding of MLOps tools and practices (e.g. MLflow Kubeflow Azure ML) for scalable deployment monitoring and retraining of AI models;

• Demonstrated ability to implement and uphold AI governance compliance and ethical AI standards across the model lifecycle;

• Strong integration knowledge including experience integrating AI services into enterprise applications and workflows; familiarity withModel Context Protocol (MCP)is ;

• Hands-on experience integrating AI systems via MCP protocol APIs and microservices;

• Skilled in conducting exploratory data analysis (EDA) and developing data visualizations using tools such as Tableau or Power BI;

• Experience mentoring and supporting junior engineers fostering technical growth and collaboration across teams;

• Strategic mindset with the ability to identify and evaluate opportunities for AI-driven innovation and business impact;

• Proficiency in using version control systems like GitHub for collaborative development and code management;

• Strong background in Agile development environments including sprint planning daily stand-ups and retrospectives;

• Strong problem-solving skills; simplifies complex issues;

• Effective verbal presentation and written communication skills;

• Works well independently and in teams;

• Constant learner focused on improvement and innovation;

• Actively shares knowledge and best practices and supports junior team members;

• Ability to assess business needs and identify high-impact opportunities;

• Enthusiastic about new technology and self-learning.

Please note that as of November 2nd 2025 we will be moving to a four day in office one day work from home model for current hybrid roles.

Honda Canada Inc. is committed to providing accommodation in its recruitment processes to applicants with disabilities upon request. The accommodation will take into account the applicants accessibility needs.

If you require accommodation at any time during the recruitment process please email Human Resources at or call .