Artificial Intelligence Engineer

What You Will Do

Platform development

1. Build and maintain parts of the agentic security platform, including production hardening and support for new security use cases.

2. Extend the MCP framework to connect SecOps (SIEM/SOAR), the cloud data warehouse, cloud AI services, other SOC tools, and SaaS applications (e.g. Confluence, Jira, Slack).

3. Build and improve MCP tools for SaaS and internal systems for triage, threat hunting, and intelligence work.

4. Help build an AI chatbot on the in-house security application for agency onboarding and Detection Development queries.

AI data and workflows

5. Build retrieval pipelines (RAG): ingest documents, split text, create embeddings, search, and rank results so AI answers use trusted security data.

6. Implement AI-assisted threat hunting and detection workflows from SOC and detection team requirements, using LLM agents with appropriate checks on outputs.

7. For onboarded use cases, implement new platform features and MCP tools, and build APIs and services so existing SOC tools can use those AI capabilities.

Infrastructure and delivery

8. Help maintain AI/LLM infrastructure for secure model hosting across internet, intranet, and local environments.

9. Deliver AI capabilities from requirements provided by SOC, Detection Development, and threat hunting teams - for example security insights, detection-related logic, and response suggestions - rather than defining those use cases independently.

10. Write automated tests, take part in code reviews before merge, and document your code, APIs, and MCP tools so others can maintain them.

11. Support logging and basic cost/speed monitoring for AI workflows with the Optimisation Track.

Required Experience and Skills

Experience

Area: What you need

Overall: 4-6 years in software development, AI application work, or security engineering

Software: Professional experience on production codebases

AI/LLM: Built LLM features, agents, or RAG systems (work or strong portfolio)

Independence: Can deliver assigned work with guidance; escalates design decisions

Technical skills

Area: What you need

Python: Solid Python for services, agents, and data; tests and Git

LLMs: Understands prompts, tool calling, context limits, and structured outputs

Agents: LangChain, LangGraph, LlamaIndex, CrewAI, or similar

APIs: REST APIs and integration; OAuth2 or API keys

RAG: Understands embeddings, chunking, retrieval; can build RAG pipelines

Databases: SQL basics; cloud warehouses, PostgreSQL, or vector stores

Cloud: Used at least one major cloud; exposure to managed AI APIs

Ways of working

Area: What you need

Testing: Unit and integration tests for APIs and agents

Agile: Scrum or Kanban

Documentation: Clear docs for tools and workflows

Security: Input validation, secrets handling, safe tool use

Helpful at hire

Area: What you need

Security: Interest in SOC work; SIEM/SOAR experience is a plus

Desirable Skills (Added Advantage)

• Built MCP servers or custom tools for LLM agents.

• SecOps (SIEM/SOAR) or threat intelligence platforms.

• Test sets or golden examples for agent behaviour.

• FastAPI and CI/CD pipelines.

Education

Degree in Computer Science, Computer or Electronics Engineering, Information Technology, or a related discipline.

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