Artificial Intelligence Engineer

What You Will DoPlatform development1. 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 workflows5. 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 delivery8. 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 SkillsExperienceArea: What you needOverall: 4-6 years in software development, AI application work, or security engineeringSoftware: Professional experience on production codebasesAI/LLM: Built LLM features, agents, or RAG systems (work or strong portfolio)Independence: Can deliver assigned work with guidance; escalates design decisionsTechnical skillsArea: What you needPython: Solid Python for services, agents, and data; tests and GitLLMs: Understands prompts, tool calling, context limits, and structured outputsAgents: LangChain, LangGraph, LlamaIndex, CrewAI, or similarAPIs: REST APIs and integration; OAuth2 or API keysRAG: Understands embeddings, chunking, retrieval; can build RAG pipelinesDatabases: SQL basics; cloud warehouses, PostgreSQL, or vector storesCloud: Used at least one major cloud; exposure to managed AI APIsWays of workingArea: What you needTesting: Unit and integration tests for APIs and agentsAgile: Scrum or KanbanDocumentation: Clear docs for tools and workflowsSecurity: Input validation, secrets handling, safe tool useHelpful at hireArea: What you needSecurity: Interest in SOC work; SIEM/SOAR experience is a plusDesirable 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.EducationDegree in Computer Science, Computer or Electronics Engineering, Information Technology, or a related discipline.“By proceeding with the job application, you are deemed to have read and acknowledged our Job Applicant Privacy Policy and consented to us using the personal data you shared for the purpose stated in the said policy.”