Software Engineer II- Salesforce

TD•Toronto, ON

8d•CA$81,600 - CA$115,200•Hybrid

About The Position

Join a team delivering enterprise-grade Salesforce solutions in a highly regulated, high-visibility environment where platform stability, secure design, traceability, and strong engineering discipline matter. This role supports critical business workflows and contributes to solutions that must balance speed, scale, governance, and long-term architectural integrity. The work includes scalable Salesforce development, integration patterns, and AI-enabled capabilities that require strong human-in-the-loop controls, clear trade-off analysis, and disciplined execution.

Requirements

Strong hands-on development experience with Apex, Lightning Web Components, SOQL/SOSL, platform security, and integration patterns in enterprise Salesforce environments.
Ability to break down complex requirements, document technical options and trade-offs, and design scalable, reusable solutions aligned with longer-term target state architecture.
Experience delivering solutions in environments where testing, controlled rollout, auditability, traceability, and human oversight are important to production quality and governance.
Strong understanding of API-based integrations, data flows, error handling, and secure movement of business data across systems.
Strong communication, stakeholder management, and execution skills, with the ability to work across product, architecture, risk, and delivery teams to move high-priority initiatives forward.

Nice To Haves

Experience in Financial Services Cloud, wealth or banking workflows, or other regulated enterprise domains.
Experience supporting AI-enabled Salesforce use cases, including summarization, classification, workflow assistance, retrieval, or model output operationalization with human-in-the-loop controls.
Familiarity with enterprise AI platforms, model selection, embeddings, prompt design, and responsible AI adoption patterns using approved model ecosystems.
Experience with DevOps, CI/CD, automated quality checks, release governance, and environment management in Salesforce delivery.
Ability to mentor peers, raise engineering standards, and be a strong technical voice within delivery teams.

Responsibilities

Design and build robust Salesforce solutions using Apex, Lightning Web Components, flows, integrations, and secure data access patterns for enterprise-scale use cases.
Translate complex business requirements into clean technical designs, with strong attention to scalability, reusability, performance, and supportability.
Work on solutions where reliability, auditability, and controlled data access are essential, including capabilities that interact with sensitive business workflows and governed data boundaries.
Collaborate with architects, product partners, and cross-functional teams to document options, trade-offs, sequencing, and implementation approaches before development begins.
Contribute to a future-ready engineering foundation so new use cases, models, and capabilities can be onboarded without re-engineering the entire solution.
Support development through design, build, testing, deployment, and production stabilization, with a strong focus on quality engineering and operational readiness.
Troubleshoot complex platform, integration, and data issues in partnership with delivery and support teams.
Uphold strong engineering standards across code quality, documentation, DevOps discipline, risk controls, and secure SDLC practices.
Help the team adopt AI responsibly and practically within Salesforce engineering workflows, using approved enterprise AI capabilities to improve developer productivity, design quality, summarization, classification, knowledge retrieval, and future workflow enablement—while respecting governance, access control, validation, and human review requirements.
Evaluate where AI can accelerate engineering work without compromising quality, compliance, or architectural integrity.
Design Salesforce solutions that are model agnostic where possible, so future AI capabilities can be introduced in a scalable and reusable way.
Work effectively with enterprise-approved model options such as GPT-based models and embeddings available through TD AI Platform environments, including examples like gpt-5, gpt-5-mini, gpt-4.1, gpt-4.1-mini, text-embedding-3-large, and model-router, depending on use case, cost, latency, and reasoning needs.
Understand when open-source or specialized models may be better suited for specific scenarios in approved dev/POC contexts, including examples such as Meta-Llama-3.1-70B-Instruct, Qwen2.5-14B-Instruct, granite-8b-code-base-4k, and approved embedding or safety models.
Apply sound judgment around model selection, prompt quality, validation, monitoring, and safe adoption in enterprise delivery contexts.
Build solutions that are technically strong, secure, scalable, and supportable.
Help the team move faster without compromising governance, testing discipline, or architectural quality.
Bring strong technical depth and practical judgment to both platform engineering and AI-enabled solution design.
Raise the bar for solution quality, design clarity, and execution in a high-critical environment.