Practice Architect II – AI/ML

TEKsystems

. Own architecture direction for agentic AI and full-stack solutions across assigned engagements .

Posted 10/22/2026full-timeRemote • CanadaSeniorLead💰 CA$140,000 - CA$210,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in solution architecture and system design, with a strong focus on agentic AI and production deployment. Proven ability to lead distributed engineering teams, mentor engineers, and communicate effectively with technical and non-technical stakeholders.

Highest-signal resume keywords

Solution ArchitectureAgentic AI DevelopmentProduction Delivery on AWS, Azure, or GCPCode Reviews and Quality GovernanceHands-On Software Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonTypeScript/NodeStrong SQLAI-Assisted CodingCode ReviewsEngineering StandardsSolution ArchitectureSystem DesignData OnboardingSecurity and Identity Model

Soft Skills

MentoringTechnical CommunicationStakeholder AlignmentProblem SolvingLeadership

Tools & Technologies

ContainersCI/CDIaCAgent FrameworksMCPEvaluation HarnessesRegression SuitesEmbedding PipelinesOrchestration ToolsHuman-in-the-Loop Workflows

Industry Keywords

Enterprise DataGoverned Knowledge LayerRACI DefinitionAgent SecurityData-Boundary EnforcementAuditRetrieval ArchitectureHybrid SearchVector SearchRetrieval Quality Tuning

Tech Stack

Tools & technologies

AWSAzureGoogle Cloud PlatformNode.jsPythonSQLTypeScript

About the role

Key responsibilities & impact

Own architecture direction for agentic AI and full-stack solutions across assigned engagements
Translate business and technical requirements into scalable, sustainable solution architectures
Set architecture standards and design patterns for delivery teams
Own code review and architecture review gates and make final technical trade-off decisions
Write production code for customer engagements approximately 40–60% of the time
Build reusable accelerators, frameworks, and reference architectures across engagements
Evaluate emerging technologies and architecture patterns and decide what enters the technology stack
Unblock engineering teams on implementation problems
Provide technical direction to engineering pods across concurrent engagements
Lead offshore technical leads and ensure design intent survives handoff to distributed teams
Mentor engineers on architecture, code quality, and modern engineering practices
Serve as the senior technical voice to customer architects, engineering leaders, and executives
Explain architecture decisions, risks, and trade-offs to technical and non-technical audiences
Surface technical risks and dependencies early with mitigation options
Drive alignment across customer product, engineering, and business stakeholders
Deliver production systems from enterprise data onboarding through operation

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline
10+ years in software engineering and architecture, including 3+ years as a lead or principal architect
2+ years building production AI/agentic systems deployed and operating, not pilots or proofs of concept
Experience onboarding enterprise data and building a governed knowledge or semantic layer, including entitlement and lineage
Ability to describe the security and identity model for agents acting on enterprise data
Currently coding and able to complete a hands-on technical assessment
Experience leading offshore or distributed engineering teams
Experience defining RACI across customer, onshore, and offshore teams on a live engagement
Production delivery on at least two of AWS, Azure, or GCP
Customer-facing experience at engineering-leadership and executive level
Agentic AI, AI-powered solution design, and modern application architecture
Solution architecture and system design
Hands-on software development and prototyping
AI-assisted coding at scale for production deployment
Code reviews, engineering standards, and quality governance
RAG and retrieval architecture, vector and hybrid search, embedding pipelines, and retrieval quality tuning
Agent security, identity, least-privilege tool access, data-boundary enforcement, and audit
Agent execution harnesses, orchestration, failure handling, and human-in-the-loop workflows
Evaluation harnesses, regression suites, groundedness and retrieval quality metrics, cost and latency instrumentation
Python, TypeScript/Node, strong SQL, agent frameworks such as LangGraph or LlamaIndex, MCP, containers, CI/CD, and IaC