AI Engineer (Entry)

Imizizi

Foundational knowledge of Large Language Models (LLMs) and how they are used in production
Experience with prompt engineering and designing prompt workflows and guardrails
Familiarity with Retrieval-Augmented Generation (RAG) concepts and connecting LLMs to documents/ databases
Basic software development skills (Python preferred) and version control (Git)
Ability to design and execute test plans for AI systems, including quality and latency measurements
Strong analytical and problem-solving skills with attention to detail
Clear written and verbal communication skills for working with technical and non-technical stakeholders
Knowledge of data handling best practices, security awareness, and privacy considerations

Hands-on experience with agent frameworks such as Copilot, LangGraph, or Semantic Kernel
Experience implementing RAG pipelines and vector search (e.g., FAISS, Pinecone, Milvus)
Familiarity with evaluation metrics for LLMs (hallucination measurement, relevance, answer quality)
Exposure to cloud platforms and deployment tooling (Azure, AWS, or GCP)
Understanding of MLOps/MLOps-lite practices for model deployment and monitoring
Experience integrating AI with enterprise applications (SAP, ServiceNow, SharePoint, Teams)
Experience with data engineering basics: ETL, data preprocessing and feature extraction
Familiarity with automated testing frameworks and CI/CD for AI components

Assist internal teams and business units to integrate AI solutions and tools into their systems
Support the development of AI agents using frameworks like Copilot, LangGraph, Semantic Kernel and relevant SDKs
Help design and implement RAG solutions to connect LLMs with enterprise documents and databases
Integrate AI components with enterprise systems such as SAP, ServiceNow, Teams, SharePoint and internal systems
Participate in customer discovery to understand real business processes, pain points and success criteria
Develop and run evaluation tests to measure hallucinations, output quality, latency and business value
Assist in building monitoring and alerting for AI system performance and data drift
Collaborate with AI engineers, solution architects and business stakeholders to deliver integrated solutions

Postgraduate degree (Master’s or higher) in Computer Science, Engineering, Statistics or a closely related field
Demonstrable coursework, projects, or internships involving LLMs, NLP or applied ML
Strong foundational programming skills (Python) and familiarity with AI/ML toolchains and SDKs

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