Jobs at Coderio

Build and operate scalable DataOps solutions across data engineering, cloud infrastructure, and DevOps. Develop Python, Bash, Terraform, and pipeline automation; orchestrate systems through APIs; deploy and maintain AWS infrastructure; support data modeling and documentation; establish DataOps best practices; and collaborate directly with customers to gather requirements and propose technical solutions.

Leads end-to-end data science initiatives, including statistical modeling, machine learning, feature engineering, deployment, monitoring, and optimization. Designs scalable AWS-based solutions, guides technical decisions, mentors data professionals, collaborates with business and engineering stakeholders, and develops data strategies and best practices. Requires strong Python, SQL, machine learning, cloud, communication, and technical leadership skills.

Own the architecture, development, automation, and production deployment of end-to-end machine learning solutions. Manage the ML lifecycle, evaluate cloud architectures for performance and cost efficiency, integrate with data engineering and software teams, troubleshoot complex systems, align solutions with business needs, and mentor teams on MLOps adoption.

Lead consolidation and ownership of CI/CD infrastructure using GitHub Actions and GitHub platform administration. Build standardized .NET pipelines, implement IaC (Terraform/CloudFormation), manage cloud deployments (Azure preferred), operate Docker/Kubernetes environments, and mentor junior engineers while enforcing governance and automation across banking applications.

Lead architecture and delivery of an AWS-based, event-driven Java/Spring Boot microservices platform. Drive design of messaging/streaming solutions, distributed transactions/saga patterns, API governance, observability, performance and cost optimization. Mentor engineers, set standards, guide technical decisions, and partner with product stakeholders to deliver scalable, resilient solutions.

Design, build, and maintain scalable Java/Spring Boot backend services and cloud-native microservices on AWS. Integrate AI tools to improve development, enforce code quality through testing and deep code reviews, identify technical debt, and deliver reliable production systems in an Agile environment.

Design, build, test, and deploy Spring Boot microservices on AWS. Implement event-driven publish/subscribe flows and distributed transaction patterns. Design and consume REST/webhook APIs. Maintain CI/CD and infrastructure-as-code, ensure observability (tracing, logging, metrics), integrate LLMs and agentic workflows with safety/PII controls, and own production reliability and performance.