DevOps Engineer

FOUNDING DEVOPS & MLOPS ENGINEER (GRAPH AI PLATFORM)

Company: ContexQ

Location: Singapore / Remote

At ContexQ, we are fundamentally reshaping the emerging landscape of Contextual Decision Intelligence. We leverage cutting-edge Symbolic AI, Graph AI, Vector Search, and Agentic frameworks to resolve billions of entities and relationships in real-time, uncovering hidden insights that revolutionize how global enterprises handle fraud detection, financial crime (AML), and risk management.

We are an ambitious, fast-moving startup taking on massive enterprise big data and distributed graph challenges, and we are looking for a Founding DevOps & MLOps Engineer to serve as the backbone of our platform architecture.

This is not a traditional corporate maintenance role. We are looking for an elite systems engineer with the DNA of an architect—someone who can orchestrate complex Kubernetes clusters, optimize distributed Spark and Graph Neural Network (GNN) workloads, and build production-grade GitOps pipelines for autonomous agentic reasoning modules. You will not just be managing servers; you will be operationalizing the next generation of explainable AI (XAI) for high-stakes industries.

THE MISSION

As our Founding DevOps & MLOps Engineer, you will lead the hands-on technical operationalization of a distributed graph computing solution that processes billions of records. You will bridge the gap between complex AI/ML research and rock-solid enterprise infrastructure.

Your ultimate metric is system resilience, ultra-low latency, and deployment velocity. You will build high-throughput CI/CD pipelines, automate Lakehouse operations, and create the seamless technical foundation that makes ContexQ's AI platform blisteringly fast, rock-solid, and enterprise-ready.

WHAT YOU WILL DO

• End-to-End CI/CD & GitOps Automation: Design and maintain robust CI/CD pipelines using GitHub commits, Terraform, and Kubernetes to automate testing and deployment of microservices across Dev, Test, and Production environments. Establish GitOps workflows to deploy and version-control agentic reasoning modules.

• Lakehouse & DataOps Engineering: Streamline the ingestion and processing of billions of records by automating Spark-based pipelines, service meshes, and blocking strategies (e.g., LSH). Operationalize incremental resolution supporting both real-time streaming (Kafka/Flink) and batch modes.

• MLOps & Graph AI Operationalization: Productionize Graph Neural Networks (GraphSAGE), semantic resolution models (Sentence-BERT), and risk scoring models at scale. Integrate SHAP, LIME, and real-time observability to monitor model drift, API latency, and pipeline health.

• Security & Enterprise Compliance: Integrate security scanning and automated compliance checks directly into the pipeline to meet stringent banking compliance standards for financial crime and fraud detection.

• Infrastructure Optimization: Optimize Docker image layering, Kubernetes resource allocation, and API load balancing to ensure high-performance execution of distributed graph algorithms with minimal latency.

WHO YOU ARE

• Distributed Systems Expert: Master's in Computer Science or Data Engineering with 5+ years of hands-on experience in distributed computing, large-scale Apache Spark workloads, and Kubernetes container orchestration.

• Cloud-Native & IaC Fluent: Highly proficient in Infrastructure as Code (Terraform, Kubernetes, Helm) and cloud-native microservices architecture.

• DataOps & MLOps Practitioner: Proven track record in streamlining CI/CD for big data pipelines, deploying ML models, and working with modern Lakehouse technologies (Apache Iceberg, Delta Lake) and search engines (Elasticsearch).

• A Systems Builder & Owner: You thrive in high-stakes, fast-paced environments. You take full ownership of reliability, performance, and scalability without needing a massive ops team behind you.

COMPENSATION & THE CONTEXQ UPSIDE

We are explicitly looking for top-tier engineering talent who wants to bet on themselves, act as true owners, and build foundational technology.

We structure compensation to heavily reward dedication and long-term impact. In exchange for your technical mastery and leadership, we offer competitive compensation, significant early-stage foundational equity, a high-impact remote-first culture, and a Long Term Incentive plan featuring a 75% base-salary bonus at the end of your 4th year of service.

This role is designed for a builder who wants real skin in the game. If you are looking for a low-stakes corporate job, this isn't for you. If you want to solve complex distributed graph challenges and share directly in the value you create, you belong at ContexQ.