Senior Machine Learning Engineer

Job Description

Job Description

As our portfolio of AI-driven solutions continues to expand, we’re looking for an experienced Machine Learning Engineer to join our high-impact data science team. This role offers the opportunity to work across trading, operations, and support functions—delivering production-grade machine learning systems that solve real business problems.

You’ll collaborate with data scientists, software engineers, and commercial stakeholders to design, build, and deploy models that drive decision-making and innovation. From project scoping to model deployment, you’ll have visibility and influence across the full ML lifecycle.

🔧 Core Responsibilities

• Act as a thought partner to commercial teams, identifying high-value opportunities for AI / ML applications
• Lead the design, development, and deployment of machine learning systems, with a focus on NLP , LLMs , and Generative AI
• Prioritize projects based on business impact and evolving market conditions
• Collaborate with cross-functional teams to gather requirements and align solutions with strategic goals
• Integrate ML solutions—including GenAI—into existing platforms to ensure seamless user experiences and scalable adoption
• Participate in code reviews, experiment design, and tooling decisions to maintain high engineering standards
• Share knowledge and mentor colleagues to build machine learning fluency across the organization

Master’s degree in Computer Science, Data Science, Machine Learning, or related field (Ph.D. preferred)

5–7+ years of experience developing and deploying ML models in production environments

Deep expertise in Python , with clean, modular, well-documented coding practices

Proven experience applying LLMs to solve business problems, with strong understanding of GenAI design patterns

Proficiency with ML frameworks and libraries : TensorFlow , PyTorch , Transformers

Familiarity with AWS and containerization tools like Docker

Strong grasp of ML fundamentals : deep learning, generative models, NLP techniques (sentiment analysis, entity recognition, disambiguation)

Excellent communication skills—able to translate complex technical concepts for non-technical audiences