Intermediate Full Stack Software Engineer

What You’ll Do

Full Stack Feature Delivery

• 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 — 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

LLM Feature Development

• 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

Technical Design & Problem-Solving

• 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

AI-Native Development

• 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 — not vague instructions that generate noise
• Review AI-generated code with the same rigor 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

Collaboration & Growth

• 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

What You Bring

• 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 Have's

• 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

Responsible AI

AltaML employees, contractors, and associates must be trained and well‑versed in the importance of Responsible AI and empowered to enact RAI principles by developing and deploying AI solutions. They should also be empowered to raise and escalated RAI concerns as required.

Equal Opportunities

AltaML is dedicated to fostering a safe, diverse, and inclusive workplace as an equal-opportunity employer. We welcome applications from qualified individuals of all backgrounds, encompassing ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, nationality, and educational backgrounds. If you are invited for an interview and require accommodations during the interview process, please don’t hesitate to let us know.

We Look for A-Players Who

• Express our core values
• Are hungry for knowledge
• Want to learn new skills
• Are respectful
• Collaborate with others across the whole company
• Share knowledge with coworkers
• Educate and promote AI and ML concepts both internally and externally
• Have a high work ethic and are self-motivated

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