Machine Learning Engineer

RESPONSIBILITIES:

Machine Learning Model Deployment

• Implement end-to-end machine learning systems from data ingestion to deployment and monitoring. 
• Expose models via RESTful APIs using FastAPI or Flask for integration with internal platforms. 
• Ensure models are scalable, reliable, and optimised for low-latency production use cases.

AI & Large Language Models

• Integrate Large Language Models (LLMs) into production systems for tasks such as agentic chatbot, credit decisioning, and internal tooling. 
• Deploy and manage LLM-powered services using APIs, prompt engineering, and retrieval-augmented generation (RAG) techniques. 
• Collaborate on fine-tuning, evaluation, and monitoring of LLM-based solutions.

Cloud, MLOps & Model Monitoring

• Deploy and manage ML workloads on AWS and/or GCP using cloud-native services. 
• Implement CI/CD pipelines, model versioning, and automated retraining workflows. 
• Monitor model performance, drift, and system health to ensure long-term reliability.

Data Governance & Compliance

• Ensure compliance with data privacy and security standards, when working with sensitive financial and credit data. 
• Document data sources, methodologies, and model parameters to ensure transparency and reproducibility.

•  6+ years in Product Management or Data Analytics, with a proven track record of driving growth in a FinTech or high-volume digital environment.
• Bachelor’s degree in computer science, Engineering, Mathematics, or a related field.
• Minimum of 4 years of experience in machine learning engineering or a related role. 
• Hands-on experience deploying machine learning models into production environments. 
• Strong experience with Python and ML frameworks such as Scikit-Learn, TensorFlow, or PyTorch. 
• Experience working with financial, credit, fraud, or transactional data is highly preferred. 
• Exposure to MLOps practices, monitoring, and model lifecycle management

Technical;

• Statistical Analysis & Modelling: Strong knowledge of statistical and machine learning techniques to create models that support risk assessment and lending decisions. 
• Programming & Scripting: Proficiency in Python for data manipulation, model building, and automation. 
• Cloud Computing: Experience with GCP and AWS for data storage, model deployment, and scalable computing. 
• Financial Data Analysis: Understanding of credit lending and credit risk data, with the ability to work within the regulatory constraints of financial data. 
• LLM & NLP Familiarity with large language models for analysing unstructured text data in financial contexts. 
• Tools: Python , Jupyter Notebooks, TensorFlow, PyTorch, Scikit-Learn, Apache Spark, SQL, FastApi, Flask

What to Expect in the Hiring Process:

• A preliminary phone call with the recruiter
• Technical interview 
• Assessment
• Interview with Senior members of the team
• Cultural and Behavioural Fit Interview with a member of the Executive team.