Senior Software Developer (AI)
our client is seeking a Senior AI Software Developer to lead the technical execution of AI-native projects. This is a hands-on engineering role embedded within a cross-functional delivery team, responsible for designing, building, and deploying production-grade application rewrites using AI-native tools and techniques on the Azure platform.
The role is accountable for end-to-end delivery – from requirements intake through production deployment – demonstrating that an AI-native SDLC can drive higher velocity, greater delivery confidence, and accelerated time to market relative to the current lifecycle. Responsibilities include selecting and operationalizing the AI development tool chain, establishing engineering standards for AI-assisted development, and producing reusable playbooks and reference architectures for adoption across the portfolio.
The successful candidate will bring a strong ownership mindset, challenge existing approaches, and set a high standard for both individual work and broader team engineering practices.
Responsibilities and Deliverables:
- Design, build, and deploy production-grade application rewrites using C#, ASP.NET, .NET 10, and Azure with monitoring, logging, and observability in place.
- Select, configure, and operationalize the AI development toolchain, including IDE integration (e.g., VS Code, Visual Studio), AI coding assistants (e.g., GitHub Copilot, Cursor), agentic development tools (e.g., Copilot Agent Mode), AI-assisted code review, and developer workflow automation.
- Implement agentic AI frameworks and multi-agent orchestration patterns (e.g., Semantic Kernel, AutoGen, CrewAI, LangGraph, LangChain, LlamaIndex) to deliver reusable orchestration components and workflows.
- Integrate LLM and AI capabilities into enterprise applications using Azure OpenAI, OpenAI APIs, and open-source models, progressing solutions from prototype to production-ready release.
- Develop and optimize RAG pipelines (embeddings, retrieval, re-ranking) and implement vector storage solutions (e.g., Azure AI Search, Cosmos DB, pgvector, Qdrant) to meet latency, cost, and quality targets.
- Implement prompt engineering strategies, prompt versioning, memory management, and task chaining with evaluation coverage using frameworks such as PromptFlow or Prompty.
- Design and execute AI evaluation and quality assurance processes using LLM eval frameworks (e.g., Azure AI Evaluation SDK, DeepEval), including automated regression suites, red-teaming, safety testing, and quality gates for AI-generated outputs.
- Implement AI observability and tracing using Azure Monitor, Application Insights, LangSmith, MLflow Tracing (OpenTelemetry), and Weights & Biases for end-to-end request logging, latency tracking, and trace correlation.
- Develop an AI-Native SDLC Playbook covering the full lifecycle from requirements intake through production deployment, documenting methodology, roles, decision points, trade-offs, lessons learned, and reusable templates.
- Define and implement the release and deployment process, ensuring AI agents and AI-assisted development activities operate within existing enterprise guardrails, including change management, approvals, automated test gates, deployment controls, rollback procedures, and production readiness reviews.
- Integrate application quality, risk, and release-readiness controls into the pipeline, including static analysis, dependency scanning, secret detection, code quality checks, and review gates for AI-generated code.
- Produce a measurable comparison of the AI-native SDLC against the current lifecycle, covering delivery velocity, defect density, automation coverage, and cost-to-deliver.
- Present pilot outcomes, quantified benefits, risks, and recommendations on scaling the AI-native SDLC methodology across future application rewrites.
- Deliver a reusable AI-Native SDLC adoption package, including playbook, reference architecture, toolchain configuration, delivery templates, governance checkpoints, security gates, and production deployment checklist.
- Mentor developers through code reviews, pairing, and internal knowledge-sharing.
Mandatory Requirements:
- Undergraduate degree in Computer Science or a related STEM (Science, Technology, Engineering or Math) discipline.
- 8+ years of software development experience, including recent work with LLMs or AI integration (an equivalent combination of education and experience may be considered).
- Experience designing AI products (LLM/RAG/agentic systems), balancing quality, latency, cost, and security/privacy.
- Proficiency in Python and experience with AI/ML frameworks (e.g., OpenAI SDKs, LangChain, Hugging Face).
- Understanding of agent-based design concepts and frameworks (e.g., Semantic Kernel, LangChain, AutoGen).
- Familiarity with RAG/GraphRAG, embeddings, and vector databases (e.g., Cosmos DB, pgvector, Qdrant).
- Experience developing and executing AI-driven tests (e.g., LLM evals, regression suites, and automated quality gates).
- Experience in C#, .NET Core, and object-oriented design.
- Experience designing and implementing cloud-ready solutions (Azure preferred).
- Knowledge of APIs, CI/CD pipelines, Git, and Agile software development practices.
- Ability to synthesize complexity and communicate AI capabilities clearly to diverse audiences.
- Strong collaboration and communication skills within cross-functional teams.
- Preferred Requirements:
- Experience with advanced agentic frameworks and multi-agent orchestration (e.g., CrewAI, LangGraph, AutoGen Studio, Semantic Kernel Agents).
- Hands-on use of AI coding assistants and agentic development tools (e.g., GitHub Copilot, Cursor) in production delivery.
- Experience with AI evaluation and benchmarking platforms (e.g., Azure AI Evaluation SDK, DeepEval).
- Familiarity with AI observability and tracing tools (e.g., LangSmith, MLflow Tracing, Weights & Biases, PromptFlow).
- Experience with prompt management and versioning (e.g., PromptFlow).
- Experience with ASP.NET and .NET 8/10 for cloud-native web applications and APIs.
- Azure platform experience including Azure App Service, Azure Functions, Container Apps, Azure SQL, Azure Key Vault, Azure DevOps, and Azure Monitor.
- Experience with Infrastructure as Code (Terraform).
- Experience in regulated industries (insurance, healthcare, government, financial services) where compliance, audit, and governance are required.
- Demonstrated ability to define and document engineering standards, including playbooks, ADRs, runbooks, and reference architectures.
Senior Software Developer (AI) Assignment Length
12 months
Senior Software Developer (AI) Assignment Location
Richmond, BC - 3 days in office
