This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more.

Role Description

This role involves leveraging a broad stack of Open Source and SaaS AI technologies to build scalable AI solutions for financial workflows.

• Utilize technologies such as Azure OpenAI, VectorDBs, RAG, RLHF, and more.
• Design, implement, and maintain cloud-native infrastructure using AWS, Azure, or GCP.
• Focus on container orchestration (EKS/ECS, GKE) and infrastructure-as-code (Terraform, CloudFormation).
• Support GPU/accelerated workloads.
• Architect and optimize advanced MLOps pipelines on Azure.
• Automate data ingestion, distributed GPU model training, versioning, CI/CD validation, AKS deployment, and monitoring.
• Conduct original research on emerging AI techniques such as meta-learning and federated learning.
• Evaluate and compare commercial and open-source LLMs for performance, cost-efficiency, alignment, and reliability.
• Lead performance optimization of large-scale AI models using techniques such as model quantization, pruning, and mixed-precision training.
• Build and deploy advanced Machine learning and statistical algorithms for financial risk forecasting and anomaly detection.
• Integrate domain knowledge and ensure model interpretability and compliance.
• Write clear, maintainable code, conduct code reviews, and mentor junior teammates.
• Evangelize automated testing, code linting, and performance monitoring.

Qualifications

• Bachelor’s degree or foreign equivalent in Statistics, Mathematics, Computer Science or related field.
• Six (6) years of progressively responsible experience in the job offered or a related occupation.
• Experience working with Machine Learning (ML) engineering, analytics, or data engineering.
• Developing advanced Machine Learning solutions including applying AI/ML techniques in financial services.
• Integrating commercial LLM APIs including OpenAI, Anthropic or Cohere into software applications.
• Experience in financial services and fintech domain including operational efficiency, reporting automation, financial data intelligence, and workflow optimization.
• Working with tools including Python, PyTorch, TensorFlow/Keras DL stacks, Hugging Face Transformers, LangChain, fine-tuning, and RLHF modern Gen-AI tooling.
• Performing AI model evaluation, prompt engineering, and Retrieval-Augmented Generation (RAG) techniques.

Requirements

• In the alternative, a Master’s degree in the above listed fields and four (4) years of experience in the above listed skills will be accepted.
• Telecommuting and/or working from home may be permissible pursuant to company policies.

Company Description