Machine Learning Engineer jobs in United States

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 hyperpersonalization using foundation models including LLMs and multimodal AI. The Machine Learning Engineer will be responsible for developing pretraining finetuning 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 decisionmaking tools.
• Plan for resourcing training and roadmap for AI adoption ensuring alignment with senior management and business needs.

• Pretrain finetune 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 multimodal 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 realtime 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 decisionmaking systems.

• Develop AI models for hyperpersonalized 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.

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 MegatronLM).
• 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.