Senior Full Stack Engineer

VYNYL•Dallas, TX

7d•$140,000 - $175,000

About The Position

Vynyl’s technologists, designers, and product strategists work on system modernization, new product development, and rescuing projects in distress. The company uses cloud-native and AI-enabled tools and combines Agile, Lean, and design thinking to address client challenges, often in regulated industries such as healthcare, financial services, government, and education. As a Senior Full Stack Developer, you will lead across the software development lifecycle, from conceptual design through deployment and monitoring. You will build robust, scalable backend services using Python and AWS serverless technologies and develop event-sourced systems that provide complete state history and auditability. The role calls for hands-on fluency with modern AI/ML development tools and the ability to thoughtfully discuss their limitations, security implications, and strategic opportunities for engineering. The position involves collaborating with product managers and designers, applying secure and reliable engineering practices, mentoring engineers, and identifying technologies and patterns that improve development velocity and system performance. Candidates must be prepared to discuss and demonstrate their practical AI tool usage, understanding of AI limitations, perspective on AI’s opportunities, and views on how AI is changing the engineering role. To apply, submit a resume and cover letter informed by the AI and forward-looking requirements. The hiring process includes an initial screening email, a technical assessment interview, and a panel interview. Salary range: $140,000–$175,000. Full-time employee benefits include health, dental, vision, and life insurance, plus a 401(k) plan with an annual 4% employer match.

Requirements

At least 5 years of professional full-stack software development experience.
Expert-level proficiency in Python backend development, including Django.
Strong working proficiency with Vue.js and TypeScript.
Practical experience designing, deploying, and maintaining applications using serverless architectures, preferably AWS Lambda, API Gateway, and managed services.
Proficiency with SQL, preferably PostgreSQL.
Understanding of event sourcing and event-driven architecture, including experience with message brokers or streaming services such as SQS, SNS, EventBridge, or Kinesis.
Experience with CI/CD pipelines, automated testing, and Infrastructure-as-Code.
Hands-on experience using AI-powered coding tools to achieve specific, quantifiable productivity gains, with an understanding of and ability to mitigate risks involving code quality, security, and intellectual property.
Ability to discuss the technical, ethical, and accuracy limitations of modern AI tools in engineering, as well as their strategic opportunities and effects on engineering workflows and roles.

Nice To Haves

Experience with advanced AWS services such as Step Functions, Kinesis, or SageMaker.
Experience working in a regulated or compliance-focused environment.
Bachelor’s or Master’s degree in Computer Science or a related technical field.

Responsibilities

Design and implement scalable, cost-efficient backend services using Python and AWS, including Lambda, API Gateway, RDS, DynamoDB, SQS, and EventBridge.
Architect and build event-sourced systems and event-driven pipelines, ensuring reliable message processing, idempotency, and eventual consistency across distributed services.
Collaborate with product managers and designers to translate requirements into technical designs and shippable code.
Implement security, logging, monitoring, and infrastructure-as-code best practices, using tools such as Terraform or CloudFormation.
Perform code reviews, mentor junior and mid-level engineers, and drive technical excellence across the team.
Research, propose, and integrate technologies and patterns to optimize development velocity and system performance.
Articulate a professional point of view on effectively, securely, and ethically integrating AI tools into the software development lifecycle.
Discuss and demonstrate practical AI tool usage, understanding of AI limitations, strategic opportunities, and the evolution of the engineering role during the interview process.