1 month ago
Singapore, SingaporeMid Level
Responsibilities
- Design, develop, and maintain scalable data pipelines, integrations, APIs, and platform services.
- Build and optimize batch, streaming, and event-driven data processing workflows.
- Develop reusable data assets, data services, and platform capabilities that improve data accessibility, quality, and scalability.
- Build and integrate AI-powered applications, workflows, and data services using LLMs and generative AI.
- Implement RAG, semantic search, vector retrieval, knowledge-access patterns, and integrations between AI models, enterprise data sources, APIs, and business applications.
- Support AI solution evaluation, monitoring, continuous improvement, observability, telemetry, and operational optimization.
- Apply AI guardrails, governance controls, responsible AI practices, data protection, model safety, and secure enterprise information access.
- Use AI-assisted development tools across coding, testing, debugging, documentation, code reviews, and operational support.
- Evaluate emerging technologies and contribute to innovation, architecture discussions, design reviews, and engineering best practices.
- Collaborate with product managers, designers, data scientists, QA, operations, compliance, and other cross-functional teams.
Requirements
- Bachelor's degree or 3+ years of relevant work experience.
- At least 2 years of work experience.
- Strong programming skills in Python, Java, SQL, or similar languages.
- Experience building and operating data pipelines, integrations, ETL/ELT workflows, and data processing applications.
- Experience working with structured and unstructured datasets at scale, distributed processing, event-driven architectures, or streaming technologies such as Kafka.
- Experience with cloud-native development, enterprise data platforms, data quality, governance, monitoring, observability, CI/CD pipelines, automated testing, and production support.
- Experience building or integrating AI-powered applications and understanding LLMs, prompt engineering, RAG, embeddings, vector search, semantic retrieval, and AI solution evaluation.
- Familiarity with AI security, responsible AI practices, guardrails, agentic AI solutions, intelligent workflows, and AI platform engineering practices.
- Experience using AI-assisted development tools such as GitHub Copilot, Copilot Agents, Claude Code, Cursor, Windsurf, or similar tools.
- Familiarity with LangChain, LangGraph, Semantic Kernel, Spring AI, CrewAI, AutoGen, Model Context Protocol, agent interoperability, and tool-calling architectures.
- Experience with Databricks, Snowflake, BigQuery, Synapse, Spark, vector databases, knowledge graphs, enterprise search, Kubernetes, Docker, infrastructure as code, and cloud platform services.
- Experience in Digital Marketing, Customer Engagement, Personalization, Analytics, Loyalty, or Digital Products is preferred.
- Experience building customer intelligence, audience segmentation, recommendation, measurement, or personalization solutions is preferred.
- Understanding of privacy, consent management, and responsible data use practices.
- Strong analytical, problem-solving, communication, and collaboration skills, with the ability to work in fast-paced and evolving environments.
Tech Stack
Categories
AI ApplicationsData Engineering
About Visa
Visa builds and operates a global digital payments network that connects consumers, merchants, financial institutions, and governments. It earns fees for processing card transactions and offering risk, data, and value‑added services through VisaNet and products like tokenization and tap‑to‑pay. Founded in 1958 and headquartered in Foster City, California, Visa is a public company (NYSE: V) serving clients in more than 200 countries and territories.