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 on-site)
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).
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