Role overview
We are looking for a Junior AI Engineer to join the AI team at Kefron, working on the automation and intelligence layer of our Accounts Payable platform (Kefron AP)
The AI team’s remit spans the full AP processing lifecycle, with the goal of progressively automating and augmenting every stage of AP using AI. This includes—but is not limited to—document understanding, matching, coding, analytics, reporting, AI-driven document generation, and customer interaction.
You will assist in building, testing, deploying, and monitoring AI and Agentic AI features that automate and enhance key AP workflows such as Extraction, Matching, Coding, reporting etc. This is a hands-on engineering role designed for someone early in their AI/ML career who wants exposure to production AI systems, not just experimentation.
You will work closely with senior AI engineers, product managers, and domain experts, gaining experience across the full AI lifecycle—from prototyping to production.
What you'll work on
You will gain exposure to a modern AI stack, including:
- Programming ML: Python, FastAPI, Transformers, Scikit-learn , deep learning libraries (TensorFlow, Keras, PyTorch, etc.) and modern agentic frameworks (e.g., LangChain, LlamaIndex).
- LLMs: Open-source LLMs, Claude, GPT-5
- NLP Models: BERT, RoBERTa and related architectures
- Search Retrieval: Elasticsearch, Reranking Models, RAG pipelines
- Data: DuckDB, MS SQL Server, Databricks
- Deployment: Docker, Azure (CI/CD pipelines, Container Apps, cloud services)
- Assist in designing, developing, and improving AI and ML models for AP automation use cases
- Build and evaluate NLP and document-understanding models (OCR, extraction, classification, matching)
- Support development of GenAI and RAG-based features, including chatbots and analytics assistants
- Troubleshoot, debug, and optimize complex AI systems to ensure optimal performance, reliability, and scalability in production environments.
Evaluation Observability: Implementing LLM evaluation frameworks and monitoring for latency, accuracy, and tool usage.
Perform data analysis to identify patterns, errors, and improvement opportunities • Work with domain experts to translate business problems into AI solutions
Contribute to code quality through documentation, testing, and peer reviews
Stay up to date with emerging AI/ML and GenAI trends and apply relevant learnings
*Personal Specification
Required Qualifications**:
- Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field
- 0–2 years of experience (or strong academic/project experience) working with ML or AI systems
- Strong Python programming skills
- Strong understanding of machine learning concepts such as model training, evaluation, and feature engineering
- Exposure to NLP, transformers, or LLM-based systems (academic or practical)
- Basic understanding of SQL and working with structured data
- Strong problem-solving mindset and willingness to learn
What we're looking for
- Deep understanding of foundational ML concepts (gradient descent, model training, attention, embeddings, transfer learning).
- Exposure to AI Agents, RAG architectures, or chatbots
- Familiarity with Docker and cloud platforms (Azure preferred)
- Experience with Spark / PySpark or Databricks
- Experience with data visualisation tools such as Matplotlib, Plotly, or similar