Intermediate Full Stack Software Engineer

AltaML • Edmonton, AB

2d • CA$90,000 - CA$110,000 • Hybrid

About The Position

AltaML is seeking a Full Stack Software Engineer who builds software in an AI-native way, treating AI coding tools as a core part of their craft. This role involves contributing to the technical delivery of ML-powered applications across cloud services, APIs, and modern front-end frameworks, integrating AI tools into the design, build, and shipping process. The engineer will be an active contributor within their project pod, shipping features end-to-end, participating in technical design discussions, and translating business requirements into well-engineered solutions. Ownership of work, writing clean code, contributing to internal frameworks, and developing fluency with AI-assisted development are key aspects of this position. The ideal candidate is a builder who uses AI tools to move fast without compromising quality, writes clear specifications, critically reviews AI-generated code, and understands when to delegate to an agent versus handcrafting solutions. They should be curious about the role of LLMs and possess a practical, evidence-based approach.

Requirements

• Degree or equivalent work experience in Computer Science, Software Engineering, or a related technical discipline
• 3–5 years of professional full stack development experience, with a track record of shipping production features end-to-end
• Hands-on, daily-driver experience using Claude (Claude Code, claude.ai, or the Claude API), Cursor, or GitHub Copilot for real software engineering work — not just occasional use
• Strong working experience with cloud services, ideally Azure (Functions, App Service, Blob Storage, Azure OpenAI, or similar)
• Proficiency in a modern object-oriented language — C#, Python, TypeScript, or equivalent — with a clear point of view on writing clean, maintainable code
• Experience building and consuming RESTful APIs and integrating third-party services
• Solid front-end experience with a modern JavaScript/TypeScript framework (React, Vue, Angular, or similar)
• Experience writing unit and API tests as a standard part of delivery (xUnit, Pytest, Postman, or similar)
• Comfortable with Docker for local development and containerized deployments
• Proficiency with Git, including working effectively in a branch-based workflow alongside AI agents
• Experience working in an Agile environment with iterative delivery cycles
• Strong written and verbal communication skills — able to articulate technical decisions clearly to peers and participate confidently in client-facing discussions

Nice To Haves

• Experience integrating LLM APIs (Claude, OpenAI, Azure OpenAI) into product features, including prompt design and cost management
• Exposure to RAG architectures, vector databases, or tool-augmented LLM workflows
• Familiarity with agentic frameworks (LangChain, LangGraph, Autogen, or similar)
• Experience writing evaluation harnesses or regression tests for LLM-powered features
• Exposure to CI/CD pipelines and automated deployment workflows (Azure DevOps, GitHub Actions, or similar)
• Prior experience in a consulting, applied AI, or client-delivery environment
• Contributions to open-source projects or internal platforms

Responsibilities

• Implement features end-to-end across front-end, back-end, and cloud infrastructure layers, taking ownership from design through deployment.
• Build and integrate RESTful APIs and cloud-hosted services, primarily on Azure, following established architecture patterns and security standards.
• Develop front-end components using modern JavaScript/TypeScript frameworks, with attention to usability, performance, and maintainability.
• Write unit, integration, and API tests as a standard part of delivery using frameworks appropriate to the stack (xUnit, Pytest, Postman, or similar).
• Use Docker for local development, environment parity, and containerized deployments.
• Manage work in Git with clean branching, meaningful commit history, and effective collaboration with AI agents in the same workflow.
• Build features that incorporate LLM calls via the Claude API or Azure OpenAI, including prompt design, context management, response handling, and cost-aware API usage.
• Implement RAG components and tool integrations as part of product features, working within established architecture patterns and contributing to their evolution.
• Write evaluation harnesses for LLM-powered features: regression tests for prompt behaviour, output quality checks, and agent tool use validation.
• Document LLM feature behaviour clearly: what the system does, what it does not do, known failure modes, and the guardrails in place.
• Develop growing awareness of when LLM-in-the-loop is the right architecture decision versus a conventional software approach — and contribute that perspective in design discussions.
• Participate actively in epic-level and feature-level design discussions, contributing well-reasoned proposals backed by research or prototype evidence.
• Use Claude to accelerate technical research: explore design alternatives, evaluate libraries, and investigate unfamiliar domains quickly — then synthesize findings into a clear recommendation.
• Identify and flag technical risks within your work scope early, with enough supporting detail for the tech lead or architect to make an informed decision.
• Produce clear technical documentation: decision records, implementation notes, and design summaries that a future team member can act on.
• Use Claude Code and AI-assisted development tools (Cursor, GitHub Copilot, and similar) as a standard part of the engineering workflow — for prototyping, code generation, refactoring, documentation, and debugging.
• Write clear, well-structured prompts and development specs that enable AI agents to produce useful, reviewable output.
• Review AI-generated code with the same rigour as human-authored code: check for correctness, edge cases, security issues, and maintainability before merging.
• Develop growing fluency in agentic development patterns: structuring repos for agent navigation, decomposing tasks into agent-friendly units, and knowing when human authorship is the right call.
• Contribute to internal discussions on AI tooling effectiveness — share what is working, what isn’t, and help refine the team’s approach.
• Participate in code reviews constructively — give specific, actionable feedback and incorporate peer feedback into your own work without defensiveness.
• Collaborate closely with ML engineers, data engineers, and product managers within the pod, understanding adjacent work well enough to minimize integration friction.
• Contribute reusable components, utilities, and internal skills to AltaML’s shared libraries.
• Engage in sprint ceremonies, stand-ups, and retrospectives as an active team member — raise blockers early, communicate progress clearly, and contribute to continuous improvement.
• Proactively seek feedback from peers and tech leads to accelerate your own growth toward senior-level ownership and technical leadership.

Benefits

• Uncapped Vacation - For all full time, permanent employees.
• Competitive Benefits - For all full time, permanent employees.
• Office as a Resource - Hybrid work environment with state-of-the-art office spaces that ignite collaboration.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree