Quality Control jobs in Singapore

Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time, driving efficiency and agility.

Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today’s fast-changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com.

Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.

But, we also believe in balancing productivity with self-care. That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.

If this sounds right up your alley, please submit an application. We look forward to getting to know you!

Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world.

Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America.

Quartz ranked us the #1 best company for remote workers.

You will join the Workato AI Lab , which partners with industry leaders, public sector organizations, and academic institutions to build real-world AI solutions. Join our multidisciplinary team as we pioneer next-generation AI innovation for Asia’s top enterprises. From driving agentic orchestration to building robust AI governance frameworks, your work will shape the future of intelligent automation for leading organizations across the region.

We're building an AI platform that powers intelligent automation, agentic workflows, and large-scale retrieval services across our enterprise. Looking for an engineer who understands both the systems layer and the AI protocol layer.

Design and develop production-grade AI services and APIs that integrate with multiple LLM providers (OpenAI, Anthropic, open-source models). Build the core infrastructure, not just applications on top of it.

Create an enterprise-grade agentic framework for orchestration, retrieval, and collaboration between multiple AI agents. This means understanding how agents communicate, how to handle state, how to route requests efficiently.

Implement knowledge retrieval systems and semantic search using vector databases (Qdrant, ElasticSearch). You'll work on the actual retrieval logic and optimization, not just plugging in a vector DB.

Build shared Python libraries and SDKs used across multiple services. Write code that other engineers will depend on, which means it needs to be clean, well-tested, and properly documented.

Drive observability and validation for AI systems. Build monitoring that actually tells you what's happening with model performance, latency, cache hit rates, and failure modes.

You've built production services with proper observability, deployment pipelines, monitoring, and security. You know how to run reliable systems at scale, handle data-intensive workloads, and debug distributed systems when things break.

You understand how things work under the hood. Standard libraries over frameworks. You can read a protocol spec and implement against it directly. When you need to integrate with an API, you understand what's happening on the wire.

Senior-level experience in Go or Python (5+ years in one, comfortable in the other). If you're coming from Go, you should know Python well enough to work in it for the first few quarters and vice versa. You'll be writing Python for core platform work.

Strong grasp of distributed systems, API design, and data-driven architectures. You've worked with both relational and non-relational databases (PostgreSQL, Elastic, vector databases).

AI/LLM Experience

You've worked with LLMs at the protocol level. You understand message structures, caching strategies, tool calling mechanics, and streaming responses. You know what's happening during inference, not just what the SDK abstracts away.

You've integrated with OpenAI-compatible APIs, ideally building directly against the protocol rather than relying on high-level frameworks. You can evaluate an LLM response, understand token usage, and optimize for latency and cost.

Experience with semantic retrieval, vector databases, or knowledge graph architectures is valuable. You understand the tradeoffs between different retrieval strategies.

Working Style

You learn fast and stay current. You monitor what's happening in AI infrastructure globally and can evaluate new approaches quickly.

You participate in technical design discussions, code reviews, and mentor other engineers. You can explain complex technical concepts clearly to both engineers and non-technical stakeholders.

You're comfortable with the full development lifecycle: design, implementation, deployment, monitoring, and continuous improvement.

You'll work with Python, FastAPI, LLM APIs, vector databases, PostgreSQL, Kubernetes, and modern CI/CD tooling. We use frameworks where appropriate (LiteLLM, Langfuse, etc.), but prefer engineers who understand what's underneath and can work at the protocol level when needed.

To stand out in our hiring process, please take the time to respond to the Job Application Questions below with concise yet informative answers. All submissions are personally reviewed by the Hiring Team, not evaluated by AI.