QUALIFICATIONS AND SKILLS:

• Strong background in maths, statistics, and graph theory (degree centrality, eigenvalues, eigenvectors, Dijkstra’s algorithm, etc.).
• Expertise in Generative AI, including vectorization, embeddings, multi-modal AI, prompt engineering, and frameworks like LangChain and LangGraph.
• Hands-on experience with graph databases (e.g., Neo4j, PG Vector, Milvus) and traversal/query languages (Cypher, Gremlin).
• Knowledge of retrieval-augmented generation (RAG) techniques with a focus on graphs.
• Proficiency in Python and libraries like Polars, Scikit-Learn, Pandas, and Numpy.
• Familiarity with regressive optimization techniques such as Logistic Regression, Bayesian Optimization, and ARIMA.
• Basic knowledge of anomaly detection and covariate shift detection techniques.
• Understanding of FastAPI and microservice development.
• Experience with Docker for containerized deployments.
• Strong data engineering skills, including large pipeline processing, multiprocessing, and multithreading.

NICE-TO-HAVE SKILLS:

• Familiarity with vector databases like Pinecone, ChromaDB, or Milvus.
• Basic understanding of out-of-distribution analysis and semantic similarity techniques.
• Experience with multi-modal AI systems.

ABOUT KOLOMOLO

Kolomolo is a global leader in digital solutions, renowned for pushing tech boundaries. As we continue to grow, we’re seeking an experienced Generative AI Engineer to join our agile, forward-thinking team.

ROLE SUMMARY:

We are looking for a motivated Generative AI Engineer to work alongside our team on the sleep apnea project. This role involves building knowledge graphs, designing graph-based AI systems, and implementing generative AI solutions. The ideal candidate will have a strong foundation in graph theory, machine learning, and data engineering, with a willingness to learn advanced techniques and tools.

KEY RESPONSIBILITIES:

• Build and maintain knowledge graphs using graph databases like Neo4j, leveraging Cypher or Gremlin for queries.
• Process forum data into structured formats and offload it to graph databases using ETL tools.
• Develop and implement graph-based AI to convert questions/answers into a graph structure and vice versa.
• Collaborate on training AI assistants capable of answering forum queries based on the built knowledge engine.
• Learn and apply AWS Bedrock and SageMaker for deploying machine learning models.
• Work on small-scale optimization models for sleep pattern analysis and machine setups.