Machine Learning Engineer

Joining Capco means joining an organisation that is committed to an inclusive working environment where you’re encouraged to #BeYourselfAtWork. We celebrate individuality and recognize that diversity and inclusion, in all forms, is critical to success. It’s important to us that we recruit and develop as diverse a range of talent as we can. We believe that everyone brings something different to the table – so we’d love to know what makes you different.

We are/have:

• Experts in banking and payments, capital markets and wealth and asset management
• Deep knowledge in financial services offering, including e.g. Finance, Risk and Compliance, Financial Crime, Core Banking etc.
• Committed to growing our business and hiring the best talent to help us get there
• Focused on maintaining our nimble, agile and entrepreneurial culture

Machine Learning Engineer Responsibilities:

• Help identify opportunities to solve complex challenges and deliver tangible business benefit through Analytics, AI/Machine Learning solutions and skillsets.
• Apply advanced Data Science methods, extensive prototyping and experimentation methodologies in the following disciplines: Data, AI: Machine Learning.
• Work closely and collaboratively with other functions within BI & Analytics in order to assess the scalability of analytical opportunities and further development into production-level solutions.
• Running research experiments both in NLP & computer vision (for remote sensing based applications) Experience & Skills:
• Proficiency with python and the python ML tool kit along with DL frameworks
• Experience with working on end to end solutions and comfortable with cloud based ML-Ops pipelines • Experience fine-tuning deep learning models including LLMs
• Experience with GPUs and understanding of balancing costs at both training and inference
• Comfortable in deploying end to end data science focused solutions with challenging data sets
  • Proficiency/experience with ML deployment strategies, micro services and infrastructure
• ML-OPS experience with setting up CI/CD pipelines, feature engineering etls, designing feature stores and understanding mechanisms of deployment across various environments
• Experience in translating ML based outcomes to business-digestible insights