As a Machine Learning Engineer, you will be responsible for designing, building, deploying, and maintaining production-grade machine learning systems that power data-driven products and decisioning platforms. You will work closely with data scientists, data engineers, and product teams to operationalize models, ensure scalability, reliability, and performance, and support business objectives across credit lending, fraud detection, and customer intelligence.
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.
Requirements
- 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
Gmail
Yahoomail