DevOps Engineer

About ContexQ
ContexQ, a Singapore-based B2B AI startup, is dedicated to transforming financial crime, fraud, and risk management through a groundbreaking contextual decision intelligence platform. It processes billions of data points from various sources using Symbolic AI, Vector Search, Graph AI, and Agentic frameworks to deliver transparent, ethical, and actionable insights to clients.
Our mission is to empower organizations with unparalleled intelligence while maintaining the highest standards of integrity and societal impact.
This is an opportunity to solve intricate technical challenges, from real-time very large data processing to advanced analytics and optimization on large network graphs, while contributing to a mission that fights financial crime, builds customer intelligence and promotes ethical decision-making.
By joining us, you will help build a NextGen Contextual Decision Intelligence Enterprise Analytics Platform that will set a new standard for explainable AI (XAI) in high-stakes industries.
Role Overview and Core Responsibilities
The role is a hands-on technical leadership position focused on implementing and operationalising a distributed graph computing solution that processes billions of entities and relationships for a next-generation data intelligence platform using a microservices architecture.
The successful candidate will lead the optimisation and the deployment of the Entity Resolution and Network Generation services.
The engineer is expected to be performance-focused, think in distributed systems, optimize for latency and throughput, and act as a self-directed technical leader who makes architectural decisions and implements them.
What you will build / contribute to:
CI/CD & Automated Deployment Pipelines
End-to-End Automation: Design and maintain robust CI/CD pipelines that trigger from GitHub commits to execute automated building, testing, and deployment across Dev, Test, and Production environments.
Infrastructure as Code: Utilize tools like Terraform and Kubernetes to manage the lifecycle of microservices, ensuring reproducible environments for the Entity Resolution and Network Generation services.
Automated Testing & Quality Gates: Implement automated unit, integration, and performance tests within the pipeline to validate distributed Spark workloads and API contracts before deployment.
GitOps for Agentic Frameworks: Establish GitOps workflows to manage the deployment of agentic reasoning modules, ensuring that updates to autonomous logic are version-controlled and peer-reviewed.
Container Orchestration: Optimize Docker image layering and Kubernetes resource allocation to ensure high-performance execution of distributed algorithms with minimal latency.
DataOps & Platform Integration
Lakehouse Operations: Streamline the ingestion and processing of billions of records by automating the deployment of blocking strategies (e.g., LSH) and Spark-based pipelines.
Service Orchestration: Ensure seamless integration between the Entity Resolution Service and the platform's decision intelligence layers through automated service mesh configurations.
Observability & Monitoring: Implement comprehensive monitoring, logging, and alerting stacks to track model drift and pipeline health in real-time.
API Lifecycle Management: Automate the deployment of high-performance APIs, ensuring low latency for entity lookups through optimized caching and load balancing.
Secure Deployment: Integrate security scanning and compliance checks into the pipeline to meet Banking compliance standards for financial crime and fraud detection
Hybrid Processing: Operationalise incremental resolution supporting both real-time and batch modes to maintain a "living" data lake.
AI/ML & Explainability (XAI)
Graph Intelligence: Operationalise Graph Neural Networks (GNNs) and models using neighbor sampling (e.g., GraphSAGE) to analyze transaction networks for fraud.
Semantic Resolution: Operationalise AI-based Entity Resolution using fuzzy matching, semantic matching (Sentence-BERT), and clustering.
Risk Orchestration: Operationalise Risk Scoring Models combining rule-based, supervised, and unsupervised methods optimized for large-scale data.
Model Integrity: Champion model transparency and fairness by integrating SHAP, LIME, bias detection, and monitoring model drift.
Minimum Qualifications:
Master’s in Computer Science, Data Engineering, or a related technical field.
5+ years of experience in distributed computing and big data systems.
Big Data Experience: Proven ability to implement, design, and optimize large Apache Spark workloads.
Infrastructure as Code & Orchestration: Proficiency in DevOps toolchains (e.g., Terraform, Kubernetes, Helm) to automate the deployment of microservices and agentic frameworks.
DataOps Pipelines: Experience streamlining data operations and CI/CD for complex data pipelines, ensuring seamless integration between entity resolution and network generation.
Search & Pattern Matching: Familiarity with Elasticsearch or equivalent for fast search and pattern matching.
MLOps: ability to operationalise AI/ML workloads to support entity resolution and graph neural networks.
Preferred Experience:
Cloud Native Architecture: Experience in designing microservices architectures that are fault-tolerant, scalable, and follow rigorous API design principles.
Lakehouse Governance: Familiarity with managing data consistency and versioning in a lakehouse environment (e.g., Apache Iceberg, Delta Lake, or Hudi).
Distributed Graph Systems: Ability to implement graph algorithms in distributed computing frameworks such as Apache Spark, Dask, or equivalent with optimized queries and storage formats.
Scale & Performance: A proven track record in building and maintaining systems processing millions of entities and edges.
Real-time Streaming: Hands-on experience with real-time stream processing frameworks such as Kafka, Flink, Apache Beam, or Spark Streaming.
Agentic Frameworks: Exposure to orchestration frameworks for AI agents, such as LangChain, Google ADK, or CrewAI, specifically for data traversal tasks.
Domain Expertise: Basic experience with banking compliance, specifically regarding Financial Crime, Fraud, and Risk management.
Why Join Us?
This is a unique opportunity for the right candidate to solve intricate technical challenges, push the boundaries of what's possible with distributed graph computing and build a platform that fights financial crime with:
Impact: You will shape the architecture of an essential platform for enterprise decision-making.
Innovation: The role offers the chance to work on advanced network graph technologies, big data pipelines, and cutting-edge AI/ML technologies.
Growth: You will influence core engineering and play a central role in building the early team.
Learning: ContexQ provides comprehensive onboarding on its ecosystem. It offers continuous learning opportunities and mentorship from senior Data Analytics & AI practitioners.
Flexibility & Equity: The company offers competitive compensation, significant early-stage equity, learning resources, and a high-impact remote-first culture. This includes a Long Term Incentive plan with 50% of base as bonus at the end of the 4th year in service.
Join us in building the next generation of Graph analytics!
Application Process
Technical Screening: Includes full-stack, big data, and cloud architecture
Take-Home Challenge: Build an Entity Resolution module that uses Graph analytics
Technical Team Interview
Culture-add discussion with ContexQ’s leadership.

Work arrangement
Yes