Senior GenAI Engineer (Banking, 1-year renewable contract)

EVOLUTION RECRUITMENT SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

EVOLUTION RECRUITMENT SOLUTIONS PTE. LTD. in Singapore seeks a seasoned software engineer to design, build and scale production‑grade Generative AI applications for enterprise use.

You will work with LangGraph, LangChain and related orchestration frameworks to implement end‑to‑end GenAI solutions. Collaborate across application, data, platform and security teams, focusing on robust backend services, observability, tooling, and production readiness.

Qualifications

• 6+ years of software engineering experience, with recent hands‑on GenAI development.
• Proven delivery of production‑grade GenAI applications beyond demos.
• Hands‑on experience with LangGraph, LangChain, or similar orchestration frameworks.

Responsibilities

• Design, build, and enhance production‑grade GenAI applications.
• Develop GenAI apps using LangGraph, LangChain, or similar frameworks.
• Implement agentic workflows, RAG, tool calling, and prompt orchestration.
• Integrate GenAI solutions with enterprise systems and data sources.
• Design reliable backend services and ensure production readiness.
• Implement logging, tracing, monitoring, and troubleshooting for GenAI apps.
• Write clean, scalable, testable code following strong engineering practices.
• Collaborate with application, data, platform, security, and business teams.
• Own delivery and steer projects with pragmatic, hands‑on approach.

Skills

GenAI app development

Backend development

API design

Distributed systems

Observability

Kubernetes/OpenShift

Problem solving

Stakeholder collaboration

Ownership

Education

Tools

LangGraph

LangChain

Open-weight models

Kubernetes

OpenShift

Redis

Job description

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

• 6+ 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:

• RAG (Retrieval‑Augmented Generation)
• Agentic workflows
• Tool calling
• Prompt orchestration
• 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.