A Collaborative Pool of
Data Platform Engineers
Building Data & AI Systems
Stuck in the 'experience required' loop?
Our Engineering Pool provides the hands-on, collaborative production work you need to qualify for top-tier Data domain Engineering positions where you build modern data platforms that unlock AI and Analytics.
A Collaborative Engineering Pool
Building Data and AI Platforms
Local projects aren’t enough for real production systems. Our pool gives engineers the experience needed to work at scale.
Data Platforms
We build high-performance data platforms that bridge the gap between raw data and actionable insights, providing engineers with practical experience and businesses with reliable systems.
Data Pipelines
We build reliable Batch and Streaming ingestion, providing Engineers with production-grade workflow experience and businesses with high-integrity data systems.
AI Platform
We build AI-ready infrastructure that bridges the gap between development and production, providing engineers with deep integration expertise and delivering automated, monitorable AI systems for businesses.
ML Infrastructures & Workflows
We engineer end-to-end MLOps platforms that automate the training, evaluation, and deployment of models, equipping engineers with authentic machine learning workflow experience and businesses with robust, production-ready processes
Cloud Architecture
We architect cost-optimized cloud systems and secure networking infrastructures that maximize performance, providing engineers with hands-on architectural experience and businesses with resilient, scalable, and efficient environments.
Cross Functional Learning
We unify Data, Machine Learning, and Platform Engineering to create a cross-functional environment where engineers master production workflows and businesses get integrated solutions.
Your Tasks in the Pool
Data Platform Engineers
- Develop and maintain an internal platform to enable Engineers to efficiently deploy their infrastructure needs.
- Implement and manage the CI/CD pipeline, standardizing how Engineers reliably ship software within the environment.
- Architect and secure the cloud networking infrastructure that supports all data tools.
- Set up the Observability stack to monitor the performance and reliability of Data Tools and their underlying infrastructure.
- Implement a FinOps pipeline for continuous tracking of cloud spend across all engineering teams.
Data Engineers
- Design, develop, and maintain robust data systems for processing and storage.
- Engineer scalable, secure, and cost-effective ETL/ELT data pipelines.
- Construct real-time data pipelines utilizing Kafka as the source.
- Establish Data Quality Frameworks to ensure the accuracy and reliability of data.
- Provide essential data support for ML Engineer teams to train machine learning models.
- Implement pipeline observability stacks for comprehensive monitoring of ETL jobs.
Machine Learning Engineers
- Build and maintain automated platforms to manage the end-to-end life cycle of model training, experimentation, and versioning.
- Architect automated pipelines to transition models from research to production using containerization and orchestration.
- Design and scale high-performance endpoints to deliver real-time predictions with minimal latency and resource consumption.
- Implement tracking systems to detect model drift and performance degradation in live production environments.
- Manage centralized feature stores to ensure consistent and high-quality data is available for both training and inference.
A Collaborative Engineering
Pool You Can Trust
Our pool brings vetted engineers, shared standards, and structured collaboration to deliver dependable data and AI support.
Engineering Pool
Engineers collaborate across specialized teams to architect end-to-end data platforms, gaining the production-grade, cross-functional expertise required to lead modern engineering life cycles
Foundational Infrastructure
We prioritize the architecture of scalable foundational infrastructure over the immediate development of local data workflows, ensuring all core systems are collaboratively deployed through a GitOps-driven framework.
The "Production-First" Stack
Our production stack uses Amazon EKS with ArgoCD and Terraform for infrastructure, Airbyte and Airflow for pipelines, DBT for transformations, and Prometheus/Grafana for observability and many more.
Shift-Left Governance
Data Quality, Security, and FinOps are established as first-class citizens and integral parts of the process from day one, rather than being treated as subsequent considerations.
Cloud FinOps
Our focus is on enabling Engineers to build responsibly by emphasizing efficient architecture and cost awareness, thereby significantly reducing cloud expenditure.
Structured Workflow and Support
Clarity for Engineers is achieved through shared tools and our Internal Project Standard Procedures (PSP), which in turn provides businesses with organized and predictable delivery.
Join the Engineering Pool in
Three Simple Steps
A straightforward process built for clarity, quality, and collaboration.
Apply
Fill out the application form to show your interest and share your background.
Complete a Task
Receive a practical assessment task that helps evaluate your skill level for production competent work.
Defend Your Work & Join the Pool
Defend your task in a review session and join the pool once approved.
Industry-designed workflows
Ready to Build With the Pool?
Build real data and AI systems with experienced Production Competent engineers and increase your chances of getting recommended for better opportunities.
Process That Pays Off
Need Production Competent
Engineers?
Hire vetted Data Platform, Data and Machine Learning Engineers with proven production competency and real project experience.
Answers to Common Questions
Here are key details engineers and businesses often want to know about joining the pool or hiring from it.