2 days ago
Singapore, SingaporeSenior
Responsibilities
- Own complex features and subsystems end to end, from requirements and architecture through development, testing, deployment, observability, and production support.
- Design, develop, deploy, and operate cloud-native applications, backend business services, REST APIs, and data analytics solutions.
- Collaborate with Product Owners, Product Management, Data/AI teams, and engineering squads to translate business and operational needs into scalable technical solutions.
- Design and integrate AI-enabled applications and intelligent workflows using LLMs, agentic AI, enterprise APIs, and data platforms.
- Apply security, access control, grounding, guardrails, evaluation, observability, performance, and responsible-AI practices to AI-enabled solutions.
- Drive AI-assisted software engineering across requirements, design, coding, testing, reviews, documentation, and troubleshooting using Kiro or equivalent tools.
- Establish engineering standards for code quality, API design, automated testing, security, CI/CD, observability, documentation, and AI-assisted development.
- Lead design and code reviews, address architectural risks and technical debt, resolve performance bottlenecks, and ensure delivery and production quality.
- Mentor and technically guide engineers while evaluating emerging AI, cloud, data, and software engineering technologies.
Requirements
- Bachelor’s or master’s degree in Computer Science, Software Engineering, or a related discipline.
- 5–8 years of relevant software engineering experience.
- Strong full-stack development and Core Java programming skills.
- Experience designing, developing, managing, and consuming REST APIs, including Postman and API gateways.
- Experience with microservices, containers, Kubernetes, orchestration, CI/CD, and cloud-native application development.
- Understanding of database and data engineering fundamentals; PostgreSQL experience is advantageous.
- Experience or strong understanding of LLMs and Generative AI, including integrating LLM capabilities into enterprise applications and workflows.
- Understanding of agentic AI architectures, including AI agents, tool or function calling, workflow orchestration, and enterprise API and data-source integration.
- Experience building AI-driven data use cases such as conversational analytics, natural-language-to-SQL, automated insights, data summarization, and intelligent decision-support workflows.
- Familiarity with RAG, embeddings, vector databases, prompt engineering, and grounding LLMs with enterprise data is advantageous.
- Understanding of AI security, data privacy, access control, observability, and responsible LLM use in enterprise environments.
- Experience in the security software or security domain and Agile software development.
- Ability to translate complex business, operational, data, and technical requirements into robust and scalable architectures.
Benefits
- Supportive workplace emphasizing respect, trust, collaboration, passion, and employee growth.
- Location: Singapore, Singapore.
Tech Stack
Categories
About Thales
Thales designs and sells defense, aerospace, space, and digital identity/cybersecurity systems to governments, armed forces, infrastructure operators, and enterprises. Its portfolio spans avionics, radars and sensors, satellites, air traffic management, secure communications, and cybersecurity and identity solutions (bolstered by the 2019 Gemalto acquisition). Headquartered in Meudon, France, Thales is publicly traded on Euronext Paris (HO) and reported €20.6 billion in sales in 2024.