Senior Software Engineer (AI)

12-month contract | Hybrid, 3 days/week on-site in the Greater Vancouver Metropolitan Area | Must hold a valid Canadian Work Visa

Our leading public sector client is seeking a Senior AI Software Developer to lead the technical execution of AI-native software initiatives within a modern cloud environment.

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 will drive end‑to‑end delivery, from requirements intake through production deployment, while demonstrating how an AI‑native Software Development Lifecycle (SDLC) can improve delivery velocity, automation, quality, and time‑to‑market. The successful candidate will help define engineering standards for AI‑assisted development, operationalise modern AI development tooling, and create reusable playbooks and reference architectures for broader organisational adoption.

This opportunity is ideal for someone with a strong ownership mindset who is passionate about advancing AI‑enabled engineering practices within enterprise environments.

Responsibilities

AI‑Native Application Development

• Design, build, and deploy production‑grade applications and application rewrites using C#, ASP.NET, .NET 10, and Azure
• Implement monitoring, logging, observability, and operational readiness best practices across deployed solutions
• Integrate LLM and AI capabilities into enterprise applications using Azure OpenAI, OpenAI APIs, and open‑source models
• Progress AI‑enabled solutions from prototype through to production‑ready deployment
• Select, configure, and operationalise AI development tooling, including AI‑assisted IDE integrations, coding assistants, code review tooling, and workflow automation
• Implement agentic AI frameworks and orchestration patterns using technologies such as Semantic Kernel, LangChain, LangGraph, CrewAI, AutoGen, and LlamaIndex
• Develop reusable orchestration workflows and AI‑enabled engineering components
• Define and implement AI‑native engineering standards, release processes, deployment controls, and governance practices

AI Evaluation, Observability & Quality Engineering

• Design and optimise RAG pipelines, embeddings, retrieval strategies, re‑ranking, and vector storage solutions
• Implement prompt engineering strategies, memory management, prompt versioning, and task chaining methodologies
• Develop AI evaluation and testing frameworks, including regression suites, automated quality gates, red‑team testing, and safety validation
• Implement AI observability and tracing using Azure Monitor, Application Insights, LangSmith, MLflow Tracing, OpenTelemetry, and related platforms
• Integrate application quality and security controls including static analysis, dependency scanning, code quality checks, and AI‑generated code review gates

Architecture, Governance & Delivery Leadership

• Develop an AI‑Native SDLC Playbook documenting methodologies, standards, reusable templates, governance checkpoints, deployment controls, and lessons learned
• Define release management and deployment frameworks ensuring AI‑enabled development operates within enterprise governance and compliance guardrails
• Produce measurable comparisons between AI‑native delivery approaches and traditional SDLC models, including metrics around velocity, defect density, automation coverage, and cost efficiency
• Present pilot outcomes, quantified benefits, risks, and recommendations to technical and business stakeholders
• Mentor developers through code reviews, pairing sessions, and technical knowledge sharing

Qualifications

Required Experience

• 8+ years of software development experience, including recent hands‑on work with AI, LLMs, or AI‑enabled application delivery
• Strong experience designing AI‑enabled products and workflows involving LLMs, RAG, or agentic systems
• Proficiency in Python and experience with AI/ML frameworks such as OpenAI SDKs, LangChain, and Hugging Face
• Strong understanding of agent‑based design concepts and orchestration frameworks including Semantic Kernel, LangChain, or AutoGen
• Experience with vector databases and retrieval systems including Cosmos DB, pgvector, Qdrant, or similar platforms
• Hands‑on experience developing and executing AI evaluation frameworks, regression testing, and automated quality gates
• Strong experience with C#, .NET Core, ASP.NET, and object‑oriented software development
• Experience designing and implementing cloud‑native solutions within Azure environments
• Knowledge of APIs, CI/CD pipelines, Git workflows, Infrastructure as Code, and Agile delivery practices
• Ability to communicate complex AI concepts clearly to technical and non‑technical audiences
• Strong collaboration and stakeholder engagement skills

Preferred Experience

• Experience with advanced multi‑agent orchestration frameworks such as CrewAI, LangGraph, AutoGen Studio, or Semantic Kernel Agents
• Hands‑on experience using AI‑native development tools such as GitHub Copilot, Cursor, or similar AI‑assisted engineering platforms in production environments
• Experience with AI evaluation and benchmarking platforms such as Azure AI Evaluation SDK or DeepEval
• Familiarity with AI observability and tracing tools including LangSmith, MLflow Tracing, Weights & Biases, and PromptFlow
• Experience with prompt management and versioning strategies
• Experience with Terraform or other Infrastructure as Code frameworks
• Experience working within regulated industries such as healthcare, insurance, government, or financial services
• Demonstrated experience defining engineering standards, architecture decision records (ADRs), runbooks, playbooks, and reference architectures

Education & Certifications

• Undergraduate degree in Computer Science, Engineering, Data Science, or related STEM discipline, or equivalent experience

Why This Role

• Opportunity to shape enterprise‑scale AI‑native software delivery practices
• High‑impact role focused on modern AI engineering, automation, and cloud‑native development
• Exposure to advanced AI orchestration, evaluation, and observability frameworks
• Collaborative, innovation‑focused environment driving next‑generation software delivery capabilities

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