Software Engineer II (Remote in United States)

Nextech

1d•Remote

About The Position

The Software Engineer II is a mid-level individual contributor who delivers complete features and components, often independently, and supports the broader team through code reviews and collaboration. The role applies development principles, patterns, and tools to varied technical problems while balancing technical design with customer needs and business priorities. It contributes to code quality, continuous improvement, and cross-functional collaboration, and uses approved AI-enabled practices across implementation, testing, debugging, documentation, production support, and code comprehension. The engineer provides relevant context and constraints, evaluates generated alternatives, and remains accountable for the resulting work. All activities must comply with Equal Employment Opportunity laws, HIPAA, ERISA, and other applicable regulations. The role may be performed fully remotely within the United States, with optional access to company offices where available. Collaboration is primarily virtual, with occasional in-person meetings or team events and travel as needed, not to exceed 10%.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related field; or an equivalent combination of education and experience.
5 years of professional software engineering experience.
Demonstrated ability to independently deliver end-to-end features, including design, implementation, testing, and deployment.
Solid understanding of software engineering principles, such as SOLID, design patterns, data structures, and algorithms, and their practical application.
Experience with modern tooling and practices such as Git-based workflows, automated testing, CI/CD pipelines, and observability tools.
Strong verbal and written communication skills, including the ability to document designs and explain technical decisions.
Academic education, professional experience, or certifications demonstrating growing depth in the technologies and patterns used in the role and a commitment to continuous learning.

Nice To Haves

Experience working within a multi-service or microservices architecture, including service-to-service communication and resilience patterns.
Hands-on experience with one or more major cloud platforms, such as AWS, Azure, or GCP, and associated managed services.
Experience leading small sub-projects or workstreams, including coordinating tasks with peers and cross-functional partners.
Familiarity with performance optimization, profiling, and troubleshooting complex issues in production environments.
Participation in technical communities or contributions to internal tools, documentation, or open source projects.

Responsibilities

Own medium-sized features or components from design and estimation through implementation, testing, deployment, and maintenance.
Apply established design patterns, architectural principles, and best practices to deliver robust, scalable, and maintainable solutions.
Use customer context and product requirements to inform technical decisions and support usability, performance, and reliability.
Participate in on-call rotations or incident response as applicable, contributing to root-cause analysis and resolution of customer-impacting issues.
Write high-quality, testable code that adheres to coding standards, including appropriate unit, integration, and regression tests.
Conduct code reviews that provide constructive feedback, identify defects, verify AI-assisted changes, and maintain codebase consistency and quality.
Recommend and implement improvements to development processes, tooling, and team workflows, including AI-assisted approaches, using evidence to retain or retire practices.
Collaborate with product, UX, QA, and engineering team members to clarify requirements, identify dependencies, and align on delivery plans.
Communicate progress, risks, trade-offs, and technical constraints clearly and proactively to technical and non-technical stakeholders.
Support onboarding and skill development of junior engineers through pairing, informal mentoring, and knowledge sharing.
Use approved AI capabilities across implementation, testing, debugging, documentation, production support, and code comprehension; provide context and constraints, evaluate alternatives, and explain decisions.
Validate AI-assisted work against requirements, edge cases, failure paths, dependencies, tests, and coding standards.
Document verification of significant or high-risk AI-assisted changes and remain accountable for submitted work.