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Paralucent

Paralucent

Technical Lead, Agentic AI Platforms (PL856)

Company

Paralucent

Role

Technical Lead, Agentic AI Platforms (PL856)

Job type

Full-time

Found on Mokaru

6 days ago

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Salary

Not disclosed by employer

Job description

Location : Toronto, ON Work Model : Hybrid — 2 days/week downtown Toronto FTE Position

Role Overview We are seeking a Technical Lead, Agentic AI Platforms to help shape and scale the next generation of AI-enabled solutions. This role will lead the technical direction for agentic AI use cases, with a primary focus on solutions built on AWS and/or Azure, while working across internal teams and strategic partners to ensure strong execution, architectural consistency, and operational readiness.

This is a hands-on leadership role for someone who brings deep technical experience first and foremost — particularly in cloud architecture, AI-enabled system design, and enterprise integration. While domain experience in lending is helpful, especially from an operations perspective, it is not the primary requirement. The priority is someone who can quickly understand business context, translate it into scalable technical solutions, and lead delivery with confidence.

Key Responsibilities

  • Technical Leadership – Agentic AI

Lead the design and technical direction of agentic AI solutions, including orchestration patterns, multi-step workflows, reasoning loops, tool use, and human-in-the-loop controls. Define architecture patterns and engineering standards for AI-enabled applications built on AWS and/or Azure. Evaluate how agentic AI can complement or evolve existing automation and workflow capabilities. Ensure solutions are designed with scalability, resilience, observability, and governance in mind from day one.

  • Cloud & Platform Architecture

Guide solution design across cloud-native services in AWS and/or Azure, including identity, security, integration, data access, model consumption, and deployment practices. Partner with enterprise stakeholders to ensure solutions align with cloud strategy, compliance requirements, and operational standards. Help define technical guardrails for AI workloads, including access controls, monitoring, logging, and cost awareness. Support reusable patterns and engineering practices that reduce brittle or one-off implementations.

  • Technical Delivery Coordination

Provide technical direction to distributed engineering teams and implementation partners. Review proposed approaches, architecture options, and delivery plans to ensure alignment with enterprise standards and business outcomes. Support quality execution through clear technical guidance, documentation expectations, and issue/risk visibility. Foster effective collaboration across internal teams, platform groups, and implementation partners.

  • Cross-Functional Collaboration

Work closely with business, product, technology, and operations stakeholders to identify high-value use cases and prioritize delivery. Translate business needs into practical technical approaches and phased implementation plans. Support business cases that emphasize impact, including operational improvements, resilience, control, and scalability. Act as a trusted advisor on the practical application of agentic AI across the organization.

  • Governance, Quality & Continuous Improvement

Help define engineering and delivery practices that support stable production operations. Ensure technical work is measurable through clear KPIs, delivery artifacts, and transparent reporting. Promote structured issue/risk management, release readiness, and post-implementation learnings. Contribute to the evolution of internal AI/automation standards, reusable assets, and best practices.

What Success Looks Like In this role, success would look like:

A clear and scalable technical approach for agentic AI solutions across AWS and/or Azure. Strong alignment between business priorities, technical architecture, and execution. AI initiatives that are not only innovative, but also practical, governable, and production-ready. Better reuse of patterns, faster problem resolution, and stronger delivery consistency. Increased confidence in how agentic AI can be applied responsibly and effectively at scale.

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