Technical Lead

Reference #: 8606

Location:
Toronto or London (Hybrid – 3 days in office) Type: Sub-contract This posting is for an incorporated contract engagement and is not an employment position. Donna Cona Inc. is currently seeking a Technical Lead, for one of our key clients. We are looking for a hands-on Technical Lead and Senior Full-Stack Engineer who can build across the front end, back end, experience engine and AI / ML layers—and establish the engineering foundations required to deliver them reliably. In this role, you will remain hands on across the full technology stack—from React/Next.js user experiences and experience orchestration to Java / Spring Boot services, APIs, Kafka events and data integration using REST API via Apigee. You will also be able to establish cloud infrastructure and development environments and CI / CD pipelines, working with enterprise platform teams where shared services or controls are involved. Candidate MUST have the following:
- Over eight (8+) years building and operating complex enterprise digital products, including experience as a Technical Lead, Staff Engineer or senior full-stack developer;
- Demonstrated full-stack depth: Strong, recent hands-on delivery in both modern front-end engineering and backend / service development, able to move confidently across layers rather than specializing in only one;
- Deep experience with TypeScript, React and Next.js, including component architecture, state management, server-side rendering, accessibility, responsive design and front-end performance;
- Strong backend engineering experience with Java and Spring Boot, including REST APIs, microservices, asynchronous processing, data access, automated testing and production troubleshooting;
- Experience designing and integrating REST, GraphQL and event-driven services using clear contracts, authentication, resiliency patterns and graceful failure handling;
- Hands-on experience establishing cloud infrastructure, containers, infrastructure as code using Terraform, source-control workflows and CI / CD pipelines. Azure experience is preferred, Kubernetes and AWS experience are valuable;
- Strong quality-engineering capability, including test driven development, defining test strategy and automation across unit, component, contract, API, integration, end-to-end, accessibility, performance and security testing;
- Practical use of AI-assisted engineering tools—such as Claude Code, GitHub Copilot or comparable tools—to accelerate software delivery, testing, debugging and documentation within approved security and code-review controls;
- Hands-on experience delivering AI / ML-enabled product features using modern model platforms and frameworks, including retrieval, prompt engineering, structured outputs, tool use, evaluation, guardrails and production monitoring; and
- Experience building reusable platform capabilities or modernizing fragmented applications while improving delivery speed, maintainability, reliability and customer outcomes. Additional enterprise

experience:
- Experience with API gateways, Kafka or comparable event platforms, headless content, personalization, feature flags, experimentation, analytics, caching and relational or document databases;
- Ability to integrate CIAM (Okta Auth0), entitlement, consent, privacy, security and audit controls into end-to-end solutions in a regulated environment;
- Strong observability practice using logs, metrics, distributed tracing and alerting; familiarity with OpenTelemetry, Azure Monitor/Application Insights or comparable tooling;
- Working knowledge of secure software supply chains, dependency management, secrets management and controls for AI-assisted development; and
- Experience in financial services, insurance or another complex regulated environment is preferred. Candidate should have the following demonstrated

experience:
- Create reusable components and orchestration services that assemble content, customer context, data, decisions and actions into consistent, personalized journeys;
- Configure cloud infrastructure, development environments, infrastructure as code and CI / CD pipelines, partner with enterprise platform teams while remaining hands on in implementation and troubleshooting;
- Implement automated unit, component, API, integration, end-to-end, accessibility, performance and security testing, with quality gates embedded in delivery pipelines;
- Apply approved AI coding tools such as Claude Code to understand codebases, generate and refactor code, create tests, diagnose defects and automate routine development work—while maintaining human review, secure-development controls and accountability for every change;
- Develop AI-assisted and agentic product features, including retrieval, prompt engineering, structured outputs, tool integration using MCP, agent to agent interaction using MCP, RAG, evaluation, guardrails, human oversight and production monitoring. Experience using AI agent frameworks like L