AI Data Solution Engineer
On-siteNairobi, Nairobi County, Kenya
Nairobi, Nairobi County, KenyaOn-siteFull TimeCONSULTINGLarge
Full TimeLargeCONSULTING
Job Summary
Design and implement AI-powered data pipelines to extract, clean, enrich, and transform structured and unstructured data. Orchestrate large language models to automate data manipulation, validation, and cross-referencing tasks, delivering polished outputs in Excel, PDF, and Word formats. Prototype emerging AI tools by translating proofs-of-concept into scalable solutions that integrate full-stack applications connecting back-ends to modern web interfaces. Optimize relational and vector databases to support intelligent workflows at scale while contributing to best practices for code quality and documentation.
Required Qualifications
- University degree holder in an environmental or technical field such as Environmental Sciences, Information Technology, Computer Science, Engineering, Management Information Systems, or Theoretical Business
- 4-6 years of relevant experience in an AI data engineering and or EHS-related field
- Hands-on experience working with large language models (OpenAI, Anthropic, Mistral, or open‐source equivalents)
- Deep understanding of embeddings, vector search, semantic similarity, and RAG architectures
- Strong proficiency in Python with experience using async patterns, data libraries (pandas, polars), and AI SDKs
- Experience implementing AI workflows using LangChain, LlamaIndex, or similar orchestration frameworks
- Solid skills in SQL, relational data modeling, and performance optimization
- Proven ability to generate and manipulate Excel, PDF, and Word documents programmatically
- Experience building APIs using FastAPI, .NET, or Node.js and integrating them into production systems
Desired Qualifications
- Diversified information technology background and knowledge of EHS software, methodologies, domains and technology
- Front‐end development experience using Vue 3 (Composition API) and TypeScript
- Experience with vector databases and AI‐oriented data storage patterns
- Familiarity with containerization, cloud platforms, or serverless architectures
- Background in NLP, document intelligence, or data enrichment pipelines
- Exposure to monorepo or large‐scale project tooling (e.g., Turborepo, Nx)
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