Enterprise AI Engineer

Porter Airlines Inc.•Toronto, ON

1d•Hybrid

About The Position

Reporting to the Managing Director, Artificial Intelligence, the Enterprise AI Engineer will play a foundational role in establishing Porter’s enterprise AI capability. As one of the first dedicated technical roles in a newly formed AI practice, this position will be responsible for designing, building, testing, and operationalizing AI-enabled solutions that address practical business problems and create measurable value across the organization. This is an applied AI engineering role focused on designing, building, and scaling practical AI solutions from concept through operation use. The AI Engineer will be expected to own the AI solution lifecycle, which may include establishing engineering standards, evaluation methods, deployment practices, monitoring approach and technical controls required to move AI solutions safely from experimentation to operational use. This includes defining practical approaches for model selection, prompt and retrieval design, data integration, testing human-in-the-loop controls, responsible AI practices and ongoing solution performance management. The role requires strong software engineering skills, sound technical judgment, and the ability to move from ambiguous business needs to reliable, secure, and maintainable technical solutions. The AI Engineer will work across a broad range of technical domains, including software engineering, machine learning, generative AI, data engineering, model integration, workflow orchestration, and production deployment. The successful candidate does not need to be an expert in every area on day one, but must be able to learn quickly, investigate unfamiliar technical problems, evaluate options, and build practical solutions in a complex enterprise environment. The AI Engineer will partner closely with IT, including Data, Automation, Cybersecurity, Architecture, and Infrastructure, as well as business stakeholders and external vendors. This role will help define the Porter’s AI engineering standards, delivery practices, technical foundations, and approaches for safely moving AI solutions from prototype to production. In the early stages of the AI practice, this role will carry broad responsibility and will require a high degree of ownership, adaptability, curiosity, and problem-solving capability. The successful candidate will be comfortable working in a new and evolving function where priorities, tools, and solution paths may change as the Porter’s AI capability matures. This is an opportunity for a strong technical builder to help establish an enterprise AI capability from the ground up and directly shape how AI is applied across the organization.

Requirements

Post-secondary education in Computer Science, Data Science, Machine Learning, Engineering, Mathematics, Statistics, or a related technical discipline, or equivalent practical experience.
Strong hands-on software development experience, with advanced proficiency in Python and experience building production-quality code.
Experience developing, deploying, and integrating machine learning models, including model training, validation, evaluation, tuning, and monitoring.
Practical experience with generative AI, large language models, retrieval-augmented generation, semantic search, or LLM-based application development.
Experience building APIs, services, scripts, automation workflows, data pipelines, or backend processes that integrate with enterprise systems and data sources.
Strong understanding of software engineering best practices, including version control, testing, modular design, documentation, code review, error handling, and maintainability.
Experience working with structured and unstructured data, including SQL databases, files, APIs, documents, logs, and other enterprise data sources.
Experience with AI frameworks, orchestration tools, or libraries such as PyTorch, TensorFlow, scikit-learn, LangChain, LlamaIndex, Hugging Face, FastAPI, or similar technologies.
Understanding of data privacy, cybersecurity, responsible AI, model risk, governance, and enterprise control requirements.
Strong analytical and problem-solving skills, with the ability to break down ambiguous problems, evaluate options, and develop practical solutions.
Demonstrated ability to learn new technologies, frameworks, business domains, and technical concepts quickly and independently.
Ability to balance experimentation and delivery, moving quickly through prototypes while recognizing when production-grade engineering, control, and documentation are required.
Strong communication skills, with the ability to explain technical concepts, trade-offs, risks, and recommendations to both technical and non-technical stakeholders.
Ability to work independently with minimal structure while maintaining alignment with strategic priorities, enterprise standards, and business outcomes.

Nice To Haves

Experience with agentic AI patterns, tool-calling, workflow orchestration, autonomous or semi-autonomous processes, and human-in-the-loop controls is considered an asset.
Experience in a regulated, operationally complex, customer-facing, transportation, airline, financial services, or similar environment is considered an asset.

Responsibilities

Design, develop, test, and deploy AI-enabled applications, tools, workflows, and prototypes that solve real business problems across the organization.
Develop practical AI solutions that may include machine learning, generative AI, agentic AI, and custom software engineering approaches.
Support the design, development, evaluation, fine-tuning, and deployment of machine learning models where appropriate, including model selection, feature engineering, training, validation, performance monitoring, and continuous improvement.
Develop robust Python-based applications, scripts, services, APIs, and data pipelines to support AI use cases from prototype through production.
Translate ambiguous business problems into technical solutions; working with stakeholders to identify requirements, constraints, risks, success measures, and implementation options.
Work with structured and unstructured data from enterprise systems, documents, APIs, databases, SaaS platforms, and other sources to enable AI and analytics use cases.
Lead the development of reusable AI engineering patterns, coding standards, model evaluation approaches, deployment practices, monitoring processes, and documentation.
Evaluate AI tools, platforms, libraries, and vendors to determine fit-for-purpose options.
Help define technical architecture and implementation standards for Porter’s emerging AI ecosystem, including integrations with cloud platforms, internal systems, APIs, databases, and automation tools.
Identify technical risks, limitations, model failure modes, data quality issues, security concerns, and operational dependencies related to AI solutions.
Prepare technical documentation, solution designs, implementation notes, and knowledge-sharing materials to support maintainability and organizational learning.
Provide technical leadership and guidance to business teams, analysts, developers, and other stakeholders as AI capabilities are introduced across Porter.
Remain current on emerging AI technologies, development practices, and model capabilities.
Operate with a high degree of ownership in a new and evolving AI practice, taking responsibility for solving complex technical problems, learning unfamiliar tools, and helping establish the organization’s AI delivery capability.
Actively participate in Porter’s Safety Management System (SMS) by reporting hazards and incidents encountered in daily operations and promoting a culture of safety