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).

#J-18808-Ljbffr

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