Systems Engineer

About the role

Neuralcraft builds AI for the rooms where it has to be defended- healthcare, finance, insurance, and the public sector.

As a Systems Engineer, you’ll own the architecture of the AI systems we ship. That starts earlier than most engineering roles: you’ll sit with a customer’s ambiguous problem, break it down, and decide what the system should actually be- what’s a model, what’s a service, what’s a workflow, and what shouldn’t be built at all.

You’ll then lead a small team to build it. This is a hands-on role- you’ll be in the Python codebase, in the cloud console, and in the design reviews, often inside a customer’s private cloud.

What you’ll do

• Take a problem from definition to design on your own - decompose ambiguous business requirements into a system architecture with clear components, contracts, and failure modes.
• Architect and build production AI systems end to end, from data ingestion and model serving through to APIs, orchestration, and observability.
• Lead and mentor a small team of engineers - set technical direction, review designs and code, and keep the team unblocked.
• Build backend services in Python that are boring in the right ways: typed, tested, observable, and easy for the next person to change.
• Design deployments across AWS, Azure, or GCP - networking, storage, compute, and cost - including on-prem and private-cloud environments where our customers need them.
• Make and document architectural trade-offs, then defend them to founders, customer engineering teams, and the occasional auditor.
• Instrument systems for traceability - logging, tracing, and monitoring that make it possible to explain what happened and why.

What we’re looking for

• 5+ years of professional software engineering experience, with at least 2 years leading projects or small teams.
• Proven track record architecting non-trivial systems yourself - you’ve owned the design
• Strong backend engineering in Python, including API design, concurrency, testing, and packaging.
• Hands-on experience with at least one major cloud (AWS, Azure, or GCP) and the judgement to pick the right primitives for the job.
• Ability to work from a vague problem statement - ask the right questions, scope it, break it down, and turn it into a buildable solution.
• Comfort with distributed systems concerns: queues, retries, idempotency, data consistency, and graceful degradation.
• Clear communication - you can explain a design to an engineer, a founder, and a compliance officer in the same week.

Preferred

• Experience building or operating machine learning systems in production, and familiarity with MLOps practices.
• Working knowledge of model registries, experiment tracking, tracing, auditing, and ML workflow orchestration.
• Experience with containers, infrastructure-as-code, and CI/CD for AI workloads.
• Exposure to LLM-based systems - serving, evaluation, or agentic orchestration.
• Worked in a regulated domain (HIPAA, SR 11-7, model governance) or alongside a compliance/audit team.
• Open-source contributions, papers, or side projects we can look at.

Why Neuralcraft

You’ll work on consequential problems with a senior team, ship to real users in weeks, and own the outcome. We care about traceability, doing right by the people accountable for these systems, and moving at the speed of trust.