Hi Nandini, Here are the JDs for this position

Senior AI / Software Architect Hi Nandini, Here are the JDs for this position

Senior AI / Software Architect Type: Contract ·

- Term: 3 years ·
- Level: Senior ·
- Location: Canada (remote with occasional in office)

About the Role We are seeking a Senior AI / Software Architect to lead the design of AI, machine learning, and automation solutions for enterprise and public-sector clients. You will translate business needs into scalable, secure, cloud-based AI architectures, define standards for LLM and generative-AI integration, and guide teams delivering innovation and automation initiatives. This is a strategy-and-design leadership role with hands-on architecture ownership.

Key Responsibilities Analyze business processes, data sources, and initiative proposals to identify AI, ML, generative-AI, and automation opportunities.

Translate business requirements into AI/ML/LLM solution designs — model selection, data needs, integration points, and security considerations.

Design scalable, cloud-based AI architectures across Azure and/or AWS environments (logical and physical).

Define architectural patterns for LLM integration, including Retrieval-Augmented Generation (RAG) , agent-based workflows, and API integration with existing applications.

Develop target-state AI platform architecture, standards, and reusable components for consistency, scalability, and reuse.

Produce AI use-case assessments and recommendations tailored to client requirements.

Develop feasibility and risk assessments for AI/automation adoption, including sandboxing strategies and progressive assurance models.

Embed data governance, classification, privacy, and cyber-security controls into AI solution designs.

Collaborate with business, IT, data, security, enterprise-architecture, and operations stakeholders to validate designs and resolve integration issues.

Produce architecture diagrams, technical documentation, and operational runbooks; support knowledge transfer to client staff.

Required Qualifications &

Experience Senior-level experience (typically 8+ years) architecting software solutions, with recent focus on AI/ML/LLM/automation.

Demonstrated experience designing end-to-end AI solution architecture — both logical and physical.

Hands-on experience with LLM and generative-AI strategy and design : RAG, agentic patterns,



and integration approaches.

Experience providing technical direction to teams working with Microsoft Power Platform, Azure AI/ML or Azure AI Studio, AWS SageMaker, ChatGPT, and/or Cohere .

Proven track record developing AI/LLM/ML/RPA use cases and moving concepts from proof-of-concept to production.

Experience working in complex, governed environments with data classification regimes, privacy and cyber-security protocols, and formal tool-use approvals (e.g., cyber-security group or data-governance committee sign-off).

Technology &

Skills Cloud AI: Azure AI/ML, Azure AI Studio, AWS SageMaker

LLM/GenAI: RAG architectures, agent frameworks, prompt/API integration, ChatGPT, Cohere

Automation: Power Platform, RPA/IPA concepts, ML pipelines

Architecture: solution & enterprise architecture, API design, secure integration, cloud infrastructure patterns

Governance: Responsible AI, data governance/classification, privacy and security-by-design

Nice to Have Public-sector or Government of Canada delivery experience.

Familiarity with GC responsible-AI principles and accessibility standards (EN 301 549).

Relevant cloud/AI certifications (Azure, AWS).

Senior DevOps / MLOps Engineer (Technical Architect) Type: Contract ·

- Term: 3 years·
- Level: Senior ·
- Location: Canada (remote with occasional on-site)

About the Role We are seeking a Senior DevOps / MLOps Engineer to build and operate the deployment and lifecycle infrastructure for AI solutions in enterprise and public-sector environments. You will own infrastructure-as-code, CI/CD for AI artifacts, model and prompt lifecycle management, and the monitoring and observability that keep AI systems reliable, secure, and cost-effective. This is a hands-on engineering role with architecture-level ownership of AI platform operations.

Key Responsibilities Design, implement, and maintain CI/CD pipelines for AI artifacts (models, prompts, RAG indices, agents) across development, test,



and production environments.

Develop infrastructure-as-code (IaC) and automation to provision reproducible AI environments and services.

Implement model, prompt, and artifact registries with versioning, lineage tracking, and rollback mechanisms.

Integrate monitoring, observability, evaluation, and drift detection into AI pipelines — covering performance, accuracy, drift, and usage at minimum.

Support containerized and serverless runtimes for AI services and automations.

Optimize runtime performance, capacity, and cloud resource usage, including AI platform cost controls.

Design and develop operational runbooks and standard operating procedures tailored to client requirements.

Apply data privacy and cyber-security protocols across AI infrastructure and pipelines.

Support operational readiness, incident response, and post-deployment stabilization.

Produce audit-ready documentation and support knowledge transfer to client staff.

Required Qualifications &

Experience Senior-level experience (typically 8+ years) in DevOps/platform engineering, with recent focus on MLOps/LLMOps for AI workloads.

Demonstrated experience building IaC for AI environments and services .

Proven experience developing automated CI/CD pipelines for AI artifacts .

Experience implementing model/prompt/artifact registries with versioning and lineage.

Experience building monitoring and observability dashboards covering performance, drift, and usage.

Experience authoring operational runbooks and SOPs for AI/production systems.

Experience working in complex, governed environments with data privacy and cyber-security protocols.

Technology &

Skills IaC: Terraform, Bicep, or CloudFormation

CI/CD: GitHub Actions, Azure DevOps, or equivalent pipeline tooling

Containers/Runtime: Docker, Kubernetes, serverless runtimes

MLOps platforms: Azure ML, AWS SageMaker (pipelines, model registry, deployment)

Observability: monitoring/logging tooling with model-drift and usage tracking

Cloud: Azure and/or AWS

Security: privacy and cyber-security controls, secure pipeline practices

Nice to Have Public-sector or Government of Canada delivery experience.

Experience with LLMOps specifics (RAG index management, prompt lifecycle, agent deployment).

Relevant cloud/DevOps certifications (Azure, AWS, Kubernetes).

📌 Senior DevOps / MLOps Engineer (Technical Architect) (Toronto)
🏢 Idea Theorem™
📍 Toronto