Original listing text, shown exactly as published by the company.
What you’ll do in this role
- Build production-capable AI-enabled internal tools, workflow automations, agents, and integrations that solve real business problems by embedding with internal teams.
- Use modern LLM capabilities such as structured outputs, tool calling, retrieval-augmented generation, agentic workflows, prompt/context engineering and creating evals.
- Help teams translate ambiguous business problems into clear, testable, AI-assisted delivery plans.
- Build backend services, APIs, integrations, and internal applications using modern software engineering practices.
- Work closely with Product, Engineering, QA, DevOps, Data, Legal, Privacy, and Security to ensure AI-built software is safe, secure, observable, and maintainable.
- Design and implement evaluation approaches for AI systems, including test sets, human review loops, quality criteria, failure mode analysis, and monitoring.
- Create reusable playbooks, templates, prompts, Skills, MCPs and examples that help other teams adopt AI effectively.
- Help assess whether a solution should remain a lightweight internal tool, be transferred to a business owner, be hardened by Product & Engineering, or be stopped.
- Stay up to date with the rapidly evolving AI engineering landscape and translate relevant developments into practical opportunities for myTomorrows.
What you bring to the table
- 3+ years of professional software engineering experience, ideally in a product, platform, backend, fullstack, startup, or scale-up environment.
- Strong software engineering fundamentals. You have built and maintained real systems before (and before vibe-coding was thing), and you know that shipping reliable software is about more than generating code.
- Experience with backend development, ideally with Python and modern API development.
- Experience with relational databases such as MySQL, PostgreSQL, or Oracle.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Strong understanding of testing, code review, observability, security, documentation, and maintainability.
- Hands-on experience with the modern essentials of AI agents, agentic engineering and AI coding tools: LLM APIs, agents, RAG, MCPs, Skills, workflow automation and AI-enabled product development.
- Ability to work independently in ambiguous environments, while communicating clearly with technical and non-technical stakeholders.
- Strong product sense: you care about solving the actual problem, not just using the newest tool.
- A pragmatic startup mentality: you can move fast, make sensible trade-offs, and know when to prototype, when to harden, and when to stop.
- Excellent English communication skills.
Nice to have
- Experience with FastAPI, Pydantic, SQLAlchemy, uv, ruff, pre-commit, GitHub Actions, or similar tools.
- Experience with AWS-native architectures, infrastructure as code, Terraform, serverless, Kubernetes, or cloud-native platform work.
- Experience with frontend development, especially Angular, React, TypeScript, or fullstack personal projects.
- Experience building internal tools, developer tools, workflow automation, or operations tooling.
- Experience with AI evaluation frameworks, model monitoring, prompt/version management, or human-in-the-loop review systems.
- Experience with healthcare, pharma, clinical research, regulated environments, privacy-sensitive data, or compliance-heavy workflows.
- Experience with tools such as Claude Code, Codex, Cursor, GitHub Copilot, LangChain/LangGraph, LlamaIndex, n8n, or similar AI/workflow platforms.
What success in the first 6 months looks like
- You have built trust with Product, Engineering, DevOps, Data, and business stakeholders by delivering useful AI-enabled solutions without creating unmanaged technical debt.
- You have delivered 2-3 high-impact AI Acceleration missions, such as an internal tool, workflow automation, agentic engineering improvement, or AI-assisted product delivery pilot.
- You have shipped production-capable software with clear ownership, tests, documentation, observability, and security considerations.
- You have created reusable templates, prompts, scripts, or examples that other teams can use.
- You have helped teams understand where AI is genuinely useful, where it is not ready yet, and what guardrails are needed.
- You have contributed to a culture where AI-assisted engineering is judged by objective production outcomes: correctness, security, maintainability, observability, and business value.
Current tech stackWe are fully cloud-native, leveraging AWS and adopting a lean, API-first product development approach driven by modern cloud technologies and thoughtful design practices. Our current Backend Engineer posting describes a stack including Python, FastAPI, Pydantic, SQLAlchemy, MySQL/PostgreSQL, Angular, GitHub Actions, Renovate, ruff, uv, pre-commit, Docker, Docker Compose, Kubernetes, Terraform, DynamoDB, and Neo4j.
As Applied AI Engineer focused on internal use cases, you will often work in lighter-weight stacks like scripts, Skills, MCPs, APIs, workflow tools, internal dashboards rather than full product infrastructure. Familiarity with the product stack is useful context, but your day-to-day tooling will be shaped by the mission.
