Machine Learning Engineer

Credit Direct Finance Company Limited

Job Location:

Lagos - Nigeria

Monthly Salary: Not Disclosed

Posted on: 13 hours ago

Vacancies: 1 Vacancy

Job Summary

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.
Bachelors 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.

Required Experience:

RESPONSIBILITIES:Machine Learning Model DeploymentImplement 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-lat...