AI Generalist and Full Stack Developer

Role summary

AI Standards is building sovereign AI systems for enterprise environments, with work spanning AI safety, cybersecurity, quantum defence and on-premises deployment. We’re looking for a hands-on AI

Generalist with strong mathematical foundations who can connect models, data, tools and business requirements into dependable applications.

You’ll work across agent orchestration, retrieval, integrations, automation and evaluation. Working closely with the founding, AI/ML and software engineering teams, you’ll help turn complex requirements into solutions designed for Fortune 1000 environments, where security, governance and reliability are essential.

Responsibilities

Analyse business processes and technical requirements to identify where AI can deliver measurable value and where conventional software is more appropriate

Define use cases with clear inputs, outputs, data requirements, constraints and acceptance criteria

Build end-to-end workflows that combine language models, specialist models, retrieval, application logic and human review where appropriate

Develop agent workflows with explicit responsibilities, tool permissions, state management, execution limits and escalation paths

Integrate models with approved APIs, databases, document stores and enterprise applications using well-defined interfaces

Build document ingestion and retrieval pipelines, including parsing, chunking, metadata, indexing and permission-aware access

Evaluate retrieval quality, source grounding and context selection using representative examples and measurable criteria

Develop structured model interactions with validated outputs, reusable instructions and regression tests

Implement timeouts, retries, fallbacks, duplicate-action safeguards and recovery mechanisms so failures are visible and manageable

Compare models and tools against task quality, latency, operating cost, privacy and on-premises compatibility

Build evaluation datasets covering task completion, answer quality, tool-use correctness and unexpected or adversarial inputs

Run controlled pilots, gather feedback and distinguish model limitations from workflow, data-quality or usability problems

Apply tenant isolation, role-based access and appropriate data-handling requirements throughout integrations and workflows

Test for prompt injection, unintended data disclosure and unsafe tool execution, collaborating with specialists on mitigation

Work with AI/ML engineers to improve routing, model selection and escalation based on observed performance

Package successful workflows for repeatable deployment and support clear handovers to engineering and operations

Maintain architecture diagrams, integration specifications, evaluation results and operating guides so other people can maintain and extend the system

Explain technical choices, limitations and results clearly to both technical and non-technical stakeholders

What success looks like

You turn open-ended requirements into working systems with clear evidence of value. Workflows complete tasks reliably, respect permissions and data boundaries, recover gracefully from failures, and can be deployed and maintained without depending on their original creator.

Qualifications

2–8 years of relevant experience in applied AI, software engineering, automation, data science or technical solution delivery

Strong mathematical foundations, particularly probability, statistics and linear algebra, with the ability to interpret evaluation metrics and uncertainty

Hands-on Python programming skills, with practical experience using APIs, SQL, structured data and Git

Experience building integrated AI applications or automation workflows beyond standalone chatbot demonstrations

Working knowledge of retrieval-augmented generation, embeddings, tool calling, structured outputs and agent orchestration

Ability to debug across model behaviour, application logic, data pipelines and external services

Understanding of access control, data governance and secure handling of enterprise information

Ability to work through incomplete requirements, document decisions and take ownership of delivery

Relevant education in mathematics, computer science, engineering or a related field, or equivalent demonstrated expertise

Additional experience we’d value

Docker, Linux, enterprise integrations, workflow observability, local model serving, on-premises deployments, AI safety testing or cybersecurity. Experience with post-quantum security applications is welcome where relevant.

Work arrangement

Remote initially. Depending on business needs, there may be an opportunity to work on-site in Reno, Nevada, and/or Alabama, USA, after 12–18 months. Any transition would be discussed separately.

To apply

Send your CV or LinkedIn profile and an example of an AI system or workflow you’ve built. Describe the problem, the components you connected, how you measured success and what happened when something failed.

Seniority level: Entry level

Employment type: Full-time

Industries: Artificial Intelligence