Machine Learning Engineer

This is a remote position.

We represent a technology player whose digital banking platform is transforming financial services in emerging markets, making a real impact by embedding credit and savings products into the digital channels people use every day. Their data-driven technology powers MNOs, fintechs, and banks, enabling them to scale fast and drive financial inclusion for millions. For those looking to work on cutting-edge financial tech, with real-world impact, this is the opportunity for you.

With rapid growth, industry recognition, and a team that thrives on innovation, this is a chance to shape the future of finance in high-growth markets across Africa.

Our client is seeking an exceptional Machine Learning Engineer (Foundation Models Focus) to develop and scale AI and machine learning initiatives across their financial services ecosystem. This role is pivotal in driving AI-powered decision-making, automation, and hyper-personalization using foundation models, including LLMs and multi-modal AI. The Machine Learning Engineer will be responsible for developing, pre-training, fine-tuning, and optimizing foundation models while working closely with data scientists, data engineers, and software engineering teams to deploy scalable AI solutions. This position plays a key role in enhancing financial AI applications such as automated underwriting, fraud detection, credit scoring, and AI-powered customer engagement, ensuring measurable improvements in performance and customer experience.

• Evaluate, scope, and support the foundation models and Generative AI strategy, including potential applications in automated underwriting, alternative credit scoring, AI-powered customer interactions, fraud detection, and early-warning models.
  
• Design and develop AI-powered applications, including chatbots, virtual assistants, personalized recommendation systems, and AI-driven decision-making tools.
  
• Plan for resourcing, training, and roadmap execution for AI adoption, ensuring alignment with senior management and business needs.
  

• Pre-train, fine-tune, and optimize foundation models (e.g., GPT, LLaMA, Mistral) for various financial applications.
  
• Hyperparameter tuning for efficiency (e.g., optimization of transformer architectures, Mixture of Experts (MoE), retrieval-augmented generation (RAG)).
  
• Implement foundation model scaling techniques, such as DeepSpeed, FSDP, and quantization to enhance efficiency.
  
• Develop custom embeddings, tokenizers, and retrieval models for enhanced financial NLP and multi-modal tasks.
  
• Build pipelines for prompt engineering, reinforcement learning with human feedback (RLHF), and model alignment.
  

• Work with engineering and data teams to ensure AI Models deployment is scalable, secure, and cost-efficient.
  
• Develop efficient inference optimization strategies using ONNX, TensorRT, and Triton Inference Server.
  
• Implement MLOps best practices, including model versioning, continuous monitoring, retraining, and deployment on on-premise infrastructure or cloud (AWS, GCP, Azure).
  
• Define best practices for data collection, storage, and pipeline automation to enable AI-driven insights in financial services.
  

• Collaborate with data governance teams to ensure AI models comply with data privacy laws.
  
• Deploy real-time AI anomaly detection models to mitigate fraud risks in digital transactions.
  
• Partner with compliance teams to develop AI-driven regulatory reporting tools and automated risk alerts.
  
• Ensure ethical AI and bias mitigation techniques are integrated into foundation model-based decision-making systems
  

• Develop AI models for hyper-personalized financial services based on behavioral analysis and customer interactions.
  
• Implement AI-powered marketing segmentation, dynamic customer scoring, and next-best-action recommendation engines.
  
• Partner with AI research institutions, universities, and fintech accelerators to drive foundation models and generative AI innovation.
  
• Represent the company at global fintech and AI summits, shaping industry conversations on Generative AI in financial services.
  
• Publish AI research, case studies, and thought leadership content to establish the company as a leader in AI-driven fintech.
  

Requirements

What it takes to succeed: 

• 7+ years of experience in AI/ML, deep learning, NLP, or applied machine learning, with at least 3+ years leading AI teams.
  
• Strong expertise in foundation models, LLM architectures and generative AI.
  
• Hands-on experience with AI frameworks (PyTorch, TensorFlow, Hugging Face Transformers, DeepSpeed, Megatron-LM).
  
• Experience in scaling AI/ML models using distributed computing frameworks (Ray, Spark, Dask).
  
• Proven ability to deploy and optimize foundation models in production, including quantization, distillation, and efficient inference strategies.
  
• Strong knowledge of data governance, AI ethics, and regulatory compliance (GDPR, financial regulations).
  
• Experience working with vector databases (FAISS, Pinecone, Chroma) for retrieval-augmented generation (RAG).
  
• Familiarity with MLOps tools (MLflow, Kubeflow, Weights & Biases).
  
• Strong programming skills in Python, SQL, and cloud platforms (AWS, GCP, Azure).
  
• Ability to translate AI innovations into business-driven AI strategies for financial services.