Technical Lead Manager
New
P
Point Digital Finance, Inc.FinTech
CanadaFull-TimeManager
Salary188,080 - 207,878 CAD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- Prompt EngineeringLLM
Requirements
- 7+ years of software engineering experience building and operating production systems
- At least 2 years leading or managing engineering teams
- Proven experience developing and shipping LLM-powered, conversational AI, or agentic systems in real-world environments
- Track record of delivering customer-facing products end-to-end
- Strong ability to partner with cross-functional stakeholders (Product, Operations, CX)
- Deep systems design expertise, with a focus on building reliable, observable, real-time services
- Demonstrated technical leadership, including guiding architecture decisions, evaluating trade-offs, and ensuring high-quality execution
- Experience defining and driving technical strategy and roadmaps for AI-driven, agentic customer experiences
- Proven ability to hire, mentor, and develop high-performing engineering teams
- Hands-on experience with LLM and agent development, including prompt engineering, RAG, tool usage, and orchestration frameworks
- Comfortable making build-vs-buy decisions and rigorously evaluating third-party vendors and platforms
- Familiarity with conversational AI infrastructure such as telephony, messaging APIs, or chat platforms at scale (preferred)
- Experience with AI/ML evaluation and observability, including instrumentation for quality, drift detection, and continuous improvement (preferred)
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
Responsibilities
- Define the AI technical strategy for CX, including evaluating and selecting frameworks, platforms, and vendor solutions.
- Own the technical roadmap for Point's AI-powered customer experience — voice, email, chat, and SMS agents that handle real homeowner interactions.
- Write code alongside your team — review PRs, prototype new agent capabilities, and debug production issues.
- Partner with Product, CX, and Ops to identify where AI agents can replace or augment manual workflows, then ship it.
- Establish success metrics for agent performance and use data to prioritize the roadmap.
- Design reliable agent architectures — orchestration, tool use, guardrails, fallback handling — that work at production scale.
- Lead and grow a small, senior engineering team: set high expectations, remove blockers, create space for deep work.
- Drive observability and reliability across the AI agent stack, leveraging tools like Langfuse.
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