Sr. Software Engineer 182139

CTI Consulting•Covington, KY

About The Position

Design, develop, test, deploy, and maintain production-quality Python applications, APIs, libraries, packages, and reusable components. Apply object-oriented programming, modular architecture, design patterns, and clean-code principles to build maintainable, extensible, and scalable software. The role focuses on advanced Python development, standardized engineering practices, reusable frameworks, comprehensive testing, defect diagnosis, and sustainable remediation. Design, deploy, and support containerized applications on Kubernetes in public-cloud environments, and automate infrastructure using Infrastructure as Code. Build and maintain CI/CD workflows, and implement observability and secure-development practices to support reliable applications and platforms. Apply relational database concepts and SQL to application persistence, integrations, and data processing. Collaborate with engineering, architecture, support, and business stakeholders on technical designs, standards, and software solutions. Document designs and operational procedures, and use AI-assisted development tools to improve development, testing, documentation, code review, and troubleshooting. Success is measured by high-quality, maintainable, well-tested, production-ready Python software and improvements in reuse, automation, platform reliability, and developer productivity.

Requirements

Advanced Python proficiency, including classes, modules, functions, data structures, type hints, packaging, dependency management, exception handling, and asynchronous or parallel-processing concepts.
Experience developing enterprise Python applications, backend services, API integrations, automation tools, internal frameworks, or platform-engineering software is preferred.
Strong problem-solving, communication, collaboration, and influencing skills.
Focus on automation, software quality, reliability, and operational excellence.

Nice To Haves

Experience with Kubernetes troubleshooting, cloud-platform engineering, high-availability design, application performance testing, and enterprise observability platforms.
Familiarity with ETL/ELT pipelines, Apache Airflow, Apache Spark, and distributed data-processing concepts.
AWS or Azure cloud certification.

Responsibilities

Design, develop, test, deploy, and maintain production-quality Python applications, APIs, libraries, packages, and reusable components.
Apply object-oriented programming, modular architecture, design patterns, and clean-code principles.
Implement standardized patterns for logging, configuration management, command-line interfaces, input validation, error handling, and failure recovery.
Develop reusable frameworks, utilities, templates, and self-service capabilities to reduce duplication and improve developer productivity.
Write unit, integration, and regression tests using appropriate mocking, fixtures, automation, and code-coverage practices.
Diagnose software defects, runtime failures, and performance bottlenecks; conduct root-cause analysis and implement sustainable remediation.
Design, configure, deploy, and support containerized applications on Kubernetes in public-cloud environments.
Automate cloud and platform infrastructure using Infrastructure as Code tools.
Build and maintain CI/CD workflows for source control, testing, packaging, artifact management, deployment, and release automation.
Implement structured logging, metrics, dashboards, monitors, and alerts to support highly available and operationally reliable applications and platforms.
Apply secure software-development practices, including peer reviews, dependency management, vulnerability remediation, secrets protection, and secure cloud configuration.
Apply relational database concepts and SQL when designing application persistence, integrations, or data-processing solutions.
Participate in technical design reviews, architecture discussions, code reviews, and the definition and adoption of engineering standards.
Collaborate with software engineers, platform engineers, architects, support teams, and business stakeholders to translate requirements into software solutions.
Produce technical designs, application documentation, operational procedures, troubleshooting guides, and root-cause analysis records.
Use AI-assisted development tools, including coding copilots and large language models, to improve development, testing, documentation, code review, and troubleshooting.