Senior Associate, Backend Engineer (Data, AI/ML), Investment & Trading Technologies, Technology & Operations

The Team The DBS Investments and Trading Technologies (ITT) Machine Learning Team works with stakeholders across all pillars of the Global Financial Markets (GFM) Department to create proprietary data science solutions for trading, sales, and operations. We are seeking a dynamic Backend Engineer (Data, AI/ ML) who excels in fast-paced environments and possesses a solid background in software and data engineering. As a key member of our team, you will be responsible for building large-scale software systems in areas such as dynamic pricing, trading analytics, personalized marketing, and Generative AI. Responsibilities

1. Design and implement large-scale machine learning software systems.
2. Enhance system design and architecture for stability, performance, and reliability of machine learning applications.
3. Develop scalable data processing pipelines/ workflows and optimize database connections.
4. Design and integrate RESTful APIs for seamless communication with external services.
5. Maintain Python-based ML applications, libraries, and APIs using Python 3.
6. Create pipelines for continuous operation, feedback, and monitoring of ML models following MLOps best practices.

1. Bachelor's degree in computer science, software engineering, or equivalent.
2. 3-5 years of Python development experience, with expertise in Python 3.
3. Strong understanding of API development, database connectivity, and programming basics.
4. Deep knowledge of software design, computer architectures, and algorithms.
5. Familiarity with multiprocessing techniques for efficient event-based processing.
6. Experience designing and implementing large-scale distributed data systems.
7. Understanding of CI/CD pipelines, agile methodologies, and software deployment.

1. Background in statistics, data science, or machine learning.
2. Familiarity with key infra technologies like NoSQL solutions (MongoDB, ElasticSearch), Openshift, Kubernetes.
3. Experience with machine learning frameworks and MLOps tools.
4. Experience building scalable ML system architectures and big-data pipelines.
5. Proficiency in Kafka/ActiveMQ and Redis for message queueing and event-driven architectures.
6. Experience in high-concurrency application development, high-performance design, coding and performance tuning.
7. Knowledge of language models, RAG concepts, and generative AI frameworks.