Applied AI ML - Senior Associate - Machine Learning Engineer

Overview

In this role you will advance AI across financial services by building scalable data science capabilities and deploying ML services integrated with strategic systems. You will work at the intersection of research and software engineering to automate processes, prioritize expert work, and improve decision making. You’ll contribute to a growing portfolio of AI-powered products and reusable components, elevating our state of the art in NLP, computer vision, and statistical ML. This is a hands-on position that values practical impact and cross-functional collaboration.

Responsibilities

• Build scalable Data Science capabilities across multiple business use cases
• Collaborate with software engineering to design and deploy ML services integrated with strategic systems
• Research and analyze datasets using statistical and ML methods
• Communicate AI capabilities and results to technical and non-technical audiences
• Document approaches, techniques, and processes for regulatory compliance
• Collaborate with cloud and SRE teams to design and deliver production architectures
• Act as an individual contributor with potential management responsibilities depending on experience

Key requirements

• Masters or PhD in a quantitative discipline (e.g., Computer Science, Mathematics, Statistics)
• Solid foundations in statistics, optimization, and ML theory
• Familiarity with popular deep learning architectures (transformers, CNNs, autoencoders)
• NLP specialization or strong interest
• Knowledge of MLOps tooling for versioning, reproducibility, observability
• Experience maintaining and improving models over time
• Extensive experience with PyTorch and Python libraries (e.g., pandas)
• Experience containerising applications/models (Docker)
• Experience with at least one major cloud provider (Azure, AWS, GCP)
• Ability to communicate complex technical information clearly at all levels

• Clear communication with both technical and non-technical stakeholders
• Cross-functional collaboration
• Problem-solving mindset
• PyTorch and Python (pandas)
• NLP
• MLOps tooling (versioning, reproducibility, observability)

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