Intermediate Full Stack Software Engineer

About Us

AltaML is a leading North American applied AI company with extensive experience in building and operationalizing AI software solutions.

AltaML is a leading North American applied AI company with extensive experience in building and operationalizing AI software solutions. We are a company like no other - we believe in making small bets, failing fast, and being better together. We are looking for creative problem-solvers who obsess about the customer to find wins across different industries. We don't hire for fit;

we hire to add. We are looking for people who play our core values of being:
Agile, Gritty Humble, and Happy. If you're passionate about AI/ML, thrive in a dynamic environment, and want to work with a diverse team of wickedly smart people, we want to hear from you! We are looking for a Full Stack Software Engineer who builds software in an AI-native way - someone who treats Claude and the latest agentic coding tools as a core part of their craft, not a novelty.

In this role, you will contribute to the technical delivery of ML-powered applications across cloud services, APIs, and modern front-end frameworks, with Claude Code, the Claude API, and agentic workflows woven into how you design, build, and ship. You will be an active contributor within your project pod, shipping features end-to-end, participating in technical design discussions, and growing your ability to translate business requirements into well-engineered solutions.

You will take ownership of your work - writing clean, reviewable code, contributing to shared internal frameworks, and continuously developing your fluency with AI-assisted development. You will thrive in this role if you are a builder who leans on Claude Code to move fast without cutting corners. You write clear specs, review AI-generated code critically, and know when to delegate to an agent versus when to handcraft.

You are curious about where LLMs fit (and where they don't), and you bring a practical, evidence-based instinct to that question.

• 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/Type Script frameworks, with attention to usability, performance, and maintainability
• Write unit, integration, and API tests as a standard part of delivery - not an afterthought - 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, Git Hub Copilot, and similar) as a standard part of the engineering…