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.

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.
- 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.

#J-18808-Ljbffr

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