Senior GenAI Engineer

Dear Applicant,

If you or someone you know is interested, please send the CV directly to quynh.nguyen@evolutionjobs.sg (most preferred, as I may overlook some CVs due to the high volume).

Please note that visa sponsorship is not available at this time.

Key Responsibilities

• Design, build, and enhance production-grade Generative AI applications for real-world enterprise use cases.
• Develop GenAI applications using LangGraph, LangChain, or similar orchestration frameworks.
• Build and implement agentic workflows, Retrieval-Augmented Generation (RAG), tool calling, prompt orchestration, and context management.
• Work with open-weight models and hosted LLMs, including model integration and serving patterns.
• Integrate GenAI solutions with enterprise systems, APIs, backend services, data sources, and operational platforms.
• Design and implement reliable and scalable backend services supporting GenAI applications.
• Engineer solutions for production readiness, including logging, tracing, monitoring, evaluation, fallback mechanisms, error handling, and troubleshooting.
• Develop clean, maintainable, scalable, and testable code following strong software engineering practices.
• Evaluate existing technical designs, challenge weak approaches, and recommend practical and effective alternatives.
• Collaborate closely with application, data, platform, infrastructure, security, and business teams to deliver end-to-end GenAI solutions.
• Troubleshoot complex application, integration, model, and production issues and drive them through to resolution.
• Contribute to the continuous improvement of GenAI applications, architecture, engineering practices, and delivery processes.
• Work effectively in a fast-moving environment with evolving requirements and incomplete information.
• Take ownership of delivery and demonstrate a pragmatic, hands-on approach focused on building useful and reliable AI products rather than over-engineering or hype.

Key Requirements

• 10+ years of software engineering experience, with recent hands-on experience in Generative AI application development.
• Proven experience building and delivering production-grade GenAI applications, beyond prototypes, experiments, or demos.
• Strong hands-on experience with LangGraph, LangChain, or similar GenAI orchestration frameworks.

Practical experience with:

o RAG (Retrieval-Augmented Generation)

o Agentic workflows

o Tool calling

o Prompt orchestration

o Context management

• Experience integrating LLMs into real-world applications through APIs, backend services, and enterprise data sources.
• Experience working with open-weight models, or strong hands-on experimentation and understanding of open-weight model deployment.
• Strong backend development skills using Python, Java, or similar programming languages.
• Good understanding of API design, distributed systems, scalability, and production resilience patterns.
• Experience with logging, tracing, observability, monitoring, evaluation, and troubleshooting for GenAI or backend applications.
• Familiarity with containerised deployment environments such as Kubernetes or OpenShift.
• Strong software engineering discipline with the ability to write clean, maintainable, scalable, and testable code.
• Strong analytical, debugging, and problem-solving skills.
• Ability to assess technical designs critically and propose practical improvements.
• Strong communication and stakeholder management skills, with the ability to collaborate across business, application, data, infrastructure, platform, and security teams.
• High level of ownership, curiosity, enthusiasm, and willingness to get hands-on with technical details.
• Comfortable working in a fast-paced environment with evolving requirements and ambiguity.
• Pragmatic delivery mindset, with a focus on building reliable and useful solutions rather than over-engineering.

Nice to Have:

• Experience with DeepAgent or similar agent frameworks.
• Experience with Langfuse, Elastic, or similar observability/search platforms.
• Experience using Redis for caching, conversation state, rate limiting, or queue-backed workflows.
• Experience with vLLM or similar inference-serving frameworks for open-weight models.
• Experience deploying GenAI workloads across cloud or containerised environments.
• Experience in financial services or banking is not required.