Private Bank – AI Engineer – Assistant Vice President job opportunity at Deutsche Bank.

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Deutsche Bank Private Bank – AI Engineer – Assistant Vice President
Experience: General
Pattern: full-time
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degreeBachelor's (B.Sc.)
loacation Singapore, One Raffles Quay, Singapore
loacation Singapore, One..........Singapore

Job Description: Details of the Division and Team: We are seeking a dedicated and experienced individual to join our team as a AI Engineer supporting the Emerging Markets (EM) region. The successful candidate will be responsible for developing machine learning algorithms, creating data pipelines, implementing AI infrastructure, and optimizing model performance. They ensure models are scalable, reliable, and integrated into production systems while collaborating with cross-functional teams to solve complex business problems. Based in Singapore, you will act as the primary point of contact for all AI Engineering applied initiatives within the region, ensuring timely delivery of AI based or related solutions. Your role will involve collaborating closely with regional stakeholders and global teams to understand business requirements, also work with the vendors where applicable and translating them into actionable AI based solutions. Proactively engage in innovation, learning and application of the knowledge. What we will offer you: A healthy, engaged and well-supported workforce is better equipped to do their best work and, more importantly, enjoy their lives inside and outside the workplace. That’s why we are committed to providing an environment with your development and wellbeing at its center. You can expect: Flexible benefits plan including virtual doctor consultation services Comprehensive leave benefits Gender Neutral Parental Leave Hybrid working arrangement 25 days of annual paid leave, plus public holiday A culture of continuous learning to aid progression Your key responsibilities: Collaborate with EM region stakeholders to identify Business needs and translate them into workable production ready solution. Take ownership of the machine learning lifecycle, from designing, training, and testing machine learning models and deep learning neural networks to automate tasks and generate insights. Creating data transformation infrastructure, managing data ingestion, and preparing data for training. Transforming models into production-ready APIs, microservices, and software applications. Monitoring model performance, ensuring scalability, and updating systems to maintain accuracy. Building and maintaining the necessary IT infrastructure (cloud platforms like AWS/GCP, containerization) for AI development. Implement algorithms for personalization and anomaly detection aligned with compliance requirements. Working with data scientists, engineers, and product managers to define AI strategies and implement features. Ensure robust documentation of AI processes, standards, and controls in line with the Bank’s data governance policies. Participate in Agile ceremonies and contribute to sprint planning, backlog grooming, and delivery cycles. Stay current with emerging AI trends, including Generative AI and large language models (LLMs), and be prepared to integrate these advanced techniques into solutions where they can drive significant business value. Support the production of scalable and optimised AI/machine learning (ML) models Focus on building algorithms for the extraction, transformation and loading of large volumes of real time, unstructured data to deploy AI/ML solutions from theoretical data science models. Run experiments to test the performance of deployed models and identifies and resolves bugs that arise in the process. Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm. Work with the relevant software platforms in which the models are deployed. Your skills and experience: Bachelor’s or Master’s degree in Artificial Intelligence / Data Science / Computer Science / Information Technology / Programming & System Analysis / Computer Studies / data science or a related field, with a minimum of 3 years of professional experience as a AI Engineer Proficient in Python, with a strong command of advanced syntax, popular libraries (e.g., Scikit-Learn), and the ability to extend existing structures. Exhibits proficiency in Object-Oriented Programming (OOP) by implementing S.O.L.I.D. principles and in data structures & algorithms by analysing complexities and making efficient choices (e.g., Numpy vs. Pandas). Proficient in applying Generative AI by using, understanding, and tuning large language models (LLMs) for diverse scenarios. Skilled in architecting solutions that augment core models with external logic and tools (e.g., Retrieval-Augmented Generation or MCP) and in developing complex, end-to-end multi-agent systems using services like ADK, DialogFlow, or equivalent cloud services. Demonstrates basic knowledge and practical experience in core software engineering practices, including navigating operating systems, programming and querying languages (e.g., Java, SQL), version control (e.g., Git), development methodologies (Agile, Waterfall), CI/CD concepts (e.g., Jenkins), testing principles, and monitoring. Possesses a foundational understanding of design patterns, software architecture, and core cloud technologies (e.g., GCP, AWS, or Azure). Demonstrates expert-level knowledge in the theoretical and practical application of machine learning. This includes mathematical understanding of both traditional and deep learning algorithms, expert skill in hyperparameter optimization, and the ability to evaluate model performance using task-specific losses and scores. Possesses an expert grasp of the underlying mathematics and statistics essential for AI/ML, including distributions, statistical methods, and probability theory. Capable of conducting code reviews for team members if needed Shows proficiency in the end-to-end data lifecycle, including advanced data engineering across traditional and cloud databases with a focus on query optimization. Highly skilled in data preprocessing, from comprehensive cleaning and encoding to advanced feature creation. A proficient communicator, capable of translating complex technical findings into clear, influential data stories for diverse audiences, including senior leadership. Possesses a foundational ability to create data visualizations (e.g., using Tableau) to support insights. Strong analytical and problem-solving skills and Excellent communication skills, both written and verbal. Ability to work independently and collaboratively in the context of global cross-functional teams. Familiarity with Agile methodologies and user story documentation. About us and our teams: Deutsche Bank is the leading German bank with strong European roots and a global network. click here to see what we do. Deutsche Bank & Diversity We strive for a culture in which we are empowered to excel together every day. This includes acting responsibly, thinking commercially, taking initiative and working collaboratively. Together we share and celebrate the successes of our people. Together we are Deutsche Bank Group. We welcome applications from all people and promote a positive, fair and inclusive work environment. For over 150 years, our dedication to being the Global Hausbank for our clients has been driven by our people – in around 60 countries and across more than 150 nationalities. Their deep understanding, insights, expertise, and passion help our clients navigate an increasingly complex world – be it in our Corporate Bank, our Private Bank, our Investment Bank or our Asset Management (DWS) division. Together we can make a great impact for our clients at home and abroad, securing their lasting success and financial security. More information at: Deutsche Bank Careers (db.com)

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