Technology Delivery Lead

TD•Toronto, ON

2d•CA$96,900 - CA$136,800•Onsite

About The Position

Are you a technically fearless engineer-leader who sees AI not as a buzzword but as a different way of thinking — one that reshapes how teams design, build, and deliver software? Do you thrive at the intersection of architecture, data, and human-centered product delivery? As Technology Delivery Lead (TDL) embedded in a high-performing, cross-functional delivery squad at TD, you will be the technical heartbeat of the team — defining architecture, setting engineering standards, mentoring engineers, and being the first voice in the room when a new feature touches system integration, security, or data — all while championing an AI-native mindset that continuously pushes the team to work smarter, not just harder. This is not a purely hands-off role. You write code, you review PRs, you pair with engineers on hard problems, and you represent the team's technical voice in planning ceremonies and cross-team dependency conversations.

Requirements

Undergraduate degree in Computer Science, Software Engineering, or equivalent technical discipline
7+ years of relevant software engineering experience, with at least 3 years in a tech lead or architect capacity
Demonstrated experience designing and shipping production systems on Google Cloud Platform
Hands-on experience integrating LLM/generative AI APIs into production applications
Go (Golang) — handlers, middleware, REST API design, service/repository patterns
React + TypeScript, state management, REST API integration
SQL, PostgreSQL, data modeling (ERD, dimensional), ETL/ELT design
GKE, GCS, BigQuery, Cloud Run, IAM, Secret Manager, Terraform
LLM integration patterns, RAG, prompt engineering, embedding models, generative AI APIs
System design, API contracts, ADRs, sequence/component diagrams
OWASP Top 10, auth/authz patterns, threat modelling basics
PI Planning, cross-team dependency management, story refinement
Thinks differently because of AI – approaches problems by asking "what if we didn't have to code this?" or "what if the system could reason about this itself?"
Has shipped at least one AI powered feature into production and learned hard lessons from it
Can evaluation AI claims critically – knows with a model is hallucinating, understands latency/cost trade-offs, and can right-size AI involvement to the actual problem
Brings concrete proposals, not abstractions – "here is an AI-powered feature we could ship in Sprint 3" vs. "we should leverage AI"

Nice To Haves

Experience with Databricks and PySpark at enterprise scale/large scale transformation
Experience in financial services or regulated industries (data governance, audit trails, compliance requirements)
AI developer tooling (e.g. AI-assisted coding platforms) in a team setting
Vertex AI — model deployment, MLOps pipelines, model evaluation
Google Cloud Operations Suite — SLO definition, alerting, distributed tracing
Experience with human-centered design (HCD) practices and working in design-led delivery squads
Familiarity with OpenShift / Kubernetes in an on-prem or hybrid context

Responsibilities

Serve as the Solution Architect / Tech Lead (SA/TL) for the delivery squad — the final technical authority for all design decisions within the team
Design and own end-to-end system architecture including API contracts, service/repository layers, data models, and integration patterns between Go backend, React frontend, and PostgreSQL
Lead architecture reviews at the start of every new feature involving system integration, new components, or external dependencies — before a single line of code is written
Produce architectural artifacts: sequence diagrams, component diagrams, ADRs (Architecture Decision Records), and API specifications
Evaluate and articulate trade-offs between competing architectural approaches, balancing velocity, maintainability, security, and cost
Define and enforce coding standards, branching strategies, and CI/CD pipeline quality gates across the team
Champion an AI-native engineering culture — not just tooling adoption, but a fundamentally different problem-solving approach: using AI to augment requirements analysis, code generation, test automation, anomaly detection, and decision support
Leverage large language models and generative AI APIs to build intelligent features — including natural language query interfaces, intelligent summarization, and document understanding pipelines
Integrate AI-powered developer tooling into the team's SDLC to accelerate delivery without sacrificing quality
Prototype and evaluate LLM-based solutions for business problems; present build-vs-buy-vs-integrate recommendations to stakeholders
Continuously scan the AI landscape and bring forward concrete, scoped proposals — not just ideas — for how emerging capabilities can be applied to the product domain
Coach the team on prompt engineering, RAG patterns, fine-tuning trade-offs, and responsible AI use within TD's risk and compliance framework
Design and operate cloud-native workloads on Google Kubernetes Engine (GKE) — including cluster configuration, workload autoscaling, resource quotas, and deployment strategies (blue/green, canary)
Architect data storage and retrieval solutions using Google Cloud Storage (GCS), including lifecycle policies, IAM bindings, and integration patterns with backend services
Build and maintain BigQuery data pipelines and reporting views for analytics and executive dashboards
Leverage Cloud Run, Pub/Sub, Cloud Scheduler, and Secret Manager to build event-driven, serverless-adjacent features where appropriate
Implement observability stacks using Google Cloud Operations Suite (Monitoring, Logging, Trace) — defining SLOs, alerting thresholds, and runbooks
Manage infrastructure as code using Terraform for GCP resource provisioning and environment parity across dev/staging/prod
Elicit, analyze, and translate business and data requirements into complete solutions — including entity-relationship models, dimensional data models, ETL/ELT pipelines, and reporting layers
Design and implement complex ETL/ELT frameworks leveraging Databricks, PySpark, and BigQuery that meet performance, lineage, and governance requirements
Establish and enforce data quality frameworks, metadata enrichment standards, and data provenance tracking aligned to TD Enterprise Data Governance policies
Ensure privacy, security, and access control requirements are captured and implemented for all data assets
Develop and maintain knowledge of upstream data sources, their schemas, and their reliability characteristics; surface risks proactively
Collaborate with the Security / BISO role on any feature involving authentication, authorization, sensitive data, financial data, or external integrations — security review is mandatory before release
Validate authentication/authorization implementations against TD security standards and OWASP Top 10
Ensure all AI/ML integrations comply with TD's responsible AI framework and data residency requirements
Participate actively in SAFe delivery ceremonies — PI Planning, System Demos, Inspect & Adapt — as the technical representative for the team
Identify and surface cross-team technical dependencies to the Release Train Engineer (RTE) early; co-own resolution
Partner with the Product Owner (PO) before development starts on any feature or epic to ensure technical feasibility, effort accuracy, and risk visibility
Work with the Business Analyst (BA/BSA) to decompose requirements into technically sound, testable stories with clear acceptance criteria
Mentor and grow engineers within the team through pairing, code review, design critique, and knowledge-sharing sessions
Prioritize and manage own workload to deliver quality results on sprint timelines while unblocking teammates