AI Engineer

Capco

Capco is a leading global technology and management consultancy that excels in driving digital transformation within the financial services and insurance sectors. With an unwavering commitment to delivering practical solutions, we empower our clients to navigate the complexities of today's fast-paced market. Our integrated services span across major financial hubs worldwide, ensuring that we are always close to our clients and their needs.

At Capco, we possess not only the expertise but also the vision and passion necessary to lead transformative change. As we embark on an exciting growth journey, this is the perfect moment to join our team. We are expanding geographically, increasing our workforce, and poised to disrupt the consulting landscape across APAC with our entrepreneurial spirit and agile methodologies.

About the Role

We are thrilled to introduce an opportunity for a talented AI Engineer to join our Hong Kong team. This role is designed for a technology innovator who thrives on solving complex challenges and is passionate about building AI-driven solutions that transform the Financial Services industry. As an AI Engineer at Capco, you will play a pivotal role in shaping the future of banking and insurance by leveraging cutting-edge AI technologies to deliver intelligent, scalable, and compliant solutions. Your work will enable our clients to unlock new efficiencies, enhance customer experience, and stay ahead in a rapidly evolving digital landscape. If you are excited about applying advanced AI techniques to real-world business problems and driving measurable impact, we want you on our team.

Key Responsibilities

• Build multi-agent workflows to optimize business processes within banking and financial services.
• Develop and maintain software pipelines for data processing, model training, and deployment using Python-based frameworks.
• Train, fine-tune, and optimize neural network models, including LLMs, for specific use cases such as risk assessment and customer query resolution.
• Collaborate with data scientists, engineers, and business stakeholders to translate requirements into production-ready AI applications.
• Ensure AI models adhere to banking regulations, data privacy standards (e.g., GDPR, CCPA), and ethical AI practices.
• Conduct experiments, evaluate model performance, and iterate solutions to improve accuracy and efficiency.
• Monitor deployed solutions in production, troubleshooting issues, and implement updates as needed.
• Stay updated on emerging AI trends, particularly in NLP and LLMs, and contribute to knowledge sharing within the team.
• Participate in code reviews, agile ceremonies, and CI/CD processes.

Experience and Qualifications

• 3–12 years of professional experience in AI/ML engineering, with a focus on software development and deployment.
• Strong software engineering experience with Python and libraries such as TensorFlow, PyTorch, scikit-learn, and Huggingface.
• Demonstrated experience in NLP, including projects involving text classification, entity recognition, or similar.
• Hands-on experience training and fine-tuning neural network models, with a track record of optimizing for performance and scalability.
• Proven expertise working with LLMs (e.g., GPT, BERT) and building agentic solutions using frameworks like LangChain, LangGraph, or ADK.
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud) for model deployment and data management.
• Solid understanding of machine learning concepts, including supervised/unsupervised learning, evaluation metrics, and bias mitigation.
• Experience with version control systems (e.g., Git) and agile methodologies.
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.

Preferred Qualifications

• Experience in financial services domains such as fraud detection, credit scoring, or customer service automation.
• Familiarity with big data tools (e.g., Spark, Hadoop) and databases (SQL/NoSQL).
• Knowledge of graph databases and knowledge graphs.
• Understanding DevOps practices, containerization (Docker, Kubernetes), and MLOps workflows.
• Publications, open-source contributions, or certifications in AI/ML.
• Strong communication skills for presenting technical concepts to non-technical stakeholders.