Duration: 5 Months

Location: Toronto, London or Winnipeg

Overview:

Supports stability and reliability of AI-enabled production systems by assisting with fixes, validating model behavior, and participating in root cause analysis for both application and model-related issues under guidance.

Assists in troubleshooting and resolving production issues (L1/L2), including AI/ML model failures, data pipeline issues, and inference errors, escalating complex problems as needed to meet SLAs.

Monitors applications, AI models, and data pipelines, identifying anomalies such as model drift, data quality issues, or performance degradation, and supports implementation of preventive measures.

Contributes to problem management by documenting recurring issues, including AI model inaccuracies or failures, and assisting in root cause analysis and remediation.

Supports performance and stability of systems by implementing minor enhancements, model updates, prompt tuning (for generative AI), or configuration fixes under supervision.

Assists with change and release activities, including model deployments, retraining updates, and configuration changes, ensuring smooth and low-risk production releases.

Collaborates with cross-functional teams, including data scientists, ML engineers, and business stakeholders, to support issue resolution and maintain service continuity.

Maintains and updates runbooks, documentation, and knowledge articles, including AI model behavior, monitoring thresholds, and troubleshooting steps.

Supports automation initiatives by identifying repetitive operational tasks and leveraging AI, scripts, or tools (e.g., RPA, intelligent automation) to improve efficiency.

Learns and applies best practices in AI system support, including model monitoring, data governance, and responsible AI, while adhering to security and compliance standards.

Provides regular updates on assigned tasks, incidents, and AI system performance to senior team members, while continuously developing skills in AI/ML, data handling, and production support practices.

Must haves:

AI, Appian Work flow, IDP and RPA skills

Post-secondary degree or diploma in computer science, data science, AI, or a related field, or an equivalent combination of training and experience, with typically 2–3 years of experience in software development, AI/ML, or application support.

Foundational experience supporting production systems, including exposure to incident management (L1/L2), basic root cause analysis, and system operations, with awareness of AI system behaviors in production.

Working knowledge of AI/ML concepts, including model lifecycle, inference, monitoring, and basic troubleshooting of model-related issues (e.g., model drift, data quality).

Familiarity with generative AI concepts such as prompt engineering, embeddings, APIs (e.g., LLM integrations), and troubleshooting output inconsistencies in production environments.

Exposure to Appian or similar low-code platforms, including awareness of Appian RPA and IDP (Intelligent Document Processing) and their integration with AI-driven workflows.

Basic understanding of data modelling, data pipelines, and how data quality impacts AI/ML model performance.





Ability to monitor and interpret AI system metrics (e.g., accuracy, latency, error rates), identify anomalies, and elevate issues appropriately.

Ability to prioritize and manage assigned support tasks, incidents, and defects, including AI-related issues, with guidance from senior team members.

Developing skills in troubleshooting technical and AI-related issues, including debugging data pipelines, APIs, and model outputs.

Willingness to learn AI/ML tools, frameworks (e.g., Python, basic ML libraries), and operational best practices for AI systems in production (MLOps fundamentals).

Good communication skills with the ability to collaborate with technical, data science, and business teams, and clearly communicate AI system issues and resolutions.

Solid analytical and problem-solving skills, with a focus on system stability, data integrity, and continuous improvement of AI-driven solutions.

Exposure to financial services or regulated environments is an asset, including awareness of responsible AI, data governance, and compliance considerations.

Nice to have:

Familiarity with Agile practices and working knowledge of tools such as Jira, Confluence, GitLab, and ServiceNow

AI/ML or Appian-related certifications (e.g., AI fundamentals, Any Cloud AI platform) or willingness to learn and obtain certifications.

Intermediate Appian AI Developer – Intelligent Automation, IDP/RPA & Production Support - 1546

Duration: 5 Months

Location: Toronto, London or Winnipeg

Overview:

Supports stability and reliability of AI-enabled production systems by assisting with fixes, validating model behavior, and participating in root cause analysis for both application and model-related issues under guidance.

Assists in troubleshooting and resolving production issues (L1/L2), including AI/ML model failures, data pipeline issues, and inference errors, escalating complex problems as needed to meet SLAs.

Monitors applications, AI models, and data pipelines, identifying anomalies such as model drift, data quality issues, or performance degradation, and supports implementation of preventive measures.

Contributes to problem management by documenting recurring issues, including AI model inaccuracies or failures, and assisting in root cause analysis and remediation.

Supports performance and stability of systems by implementing minor enhancements, model updates, prompt tuning (for generative AI), or configuration fixes under supervision.

Assists with change and release activities, including model deployments, retraining updates, and configuration changes, ensuring smooth and low-risk production releases.

Collaborates with cross-functional teams, including data scientists, ML engineers, and business stakeholders, to support issue resolution and maintain service continuity.

Maintains and updates runbooks,



documentation, and knowledge articles, including AI model behavior, monitoring thresholds, and troubleshooting steps.

Supports automation initiatives by identifying repetitive operational tasks and leveraging AI, scripts, or tools (e.g., RPA, intelligent automation) to improve efficiency.

Learns and applies best practices in AI system support, including model monitoring, data governance, and responsible AI, while adhering to security and compliance standards.

Provides regular updates on assigned tasks, incidents, and AI system performance to senior team members, while continuously developing skills in AI/ML, data handling, and production support practices.

Must haves:

AI, Appian Work flow, IDP and RPA skills

Post-secondary degree or diploma in computer science, data science, AI, or a related field, or an equivalent combination of training and experience, with typically 2–3 years of experience in software development, AI/ML, or application support.

Foundational experience supporting production systems, including exposure to incident management (L1/L2), basic root cause analysis, and system operations, with awareness of AI system behaviors in production.

Working knowledge of AI/ML concepts, including model lifecycle, inference, monitoring, and basic troubleshooting of model-related issues (e.g., model drift, data quality).

Familiarity with generative AI concepts such as prompt engineering, embeddings, APIs (e.g., LLM integrations), and troubleshooting output inconsistencies in production environments.

Exposure to Appian or similar low-code platforms, including awareness of Appian RPA and IDP (Intelligent Document Processing) and their integration with AI-driven workflows.

Basic understanding of data modelling, data pipelines, and how data quality impacts AI/ML model performance.

Ability to monitor and interpret AI system metrics (e.g., accuracy, latency, error rates), identify anomalies, and elevate issues appropriately.

Ability to prioritize and manage assigned support tasks, incidents, and defects, including AI-related issues, with guidance from senior team members.

Developing skills in troubleshooting technical and AI-related issues, including debugging data pipelines, APIs, and model outputs.

Willingness to learn AI/ML tools, frameworks (e.g., Python, basic ML libraries), and operational best practices for AI systems in production (MLOps fundamentals).

Good communication skills with the ability to collaborate with technical, data science, and business teams, and clearly communicate AI system issues and resolutions.

Strong analytical and problem-solving skills, with a focus on system stability, data integrity, and continuous improvement of AI-driven solutions.

Exposure to financial services or regulated environments is an asset, including awareness of responsible AI, data governance, and compliance considerations.

Nice to have:

Familiarity with Agile practices and working knowledge of tools such as Jira, Confluence, GitLab, and ServiceNow

AI/ML or Appian-related certifications (e.g., AI fundamentals, Any Cloud AI platform) or willingness to learn and obtain certifications.

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📌 Intermediate Appian AI Developer – Intelligent Automation, IDP/RPA & Production Support - 1546 (Ontario)
🏢 S.i. Systems
📍 Ontario