Senior Full Stack Engineer (Python, Serverless, AI Fluency)
$140k - $175kVYNYL
Vynyl's technologists, designers and product strategists are experts at system modernization, new product development, and rescuing projects in distress. We use the best cloud-native and AI-enabled tools, and apply a battle-tested combination of Agile, Lean, and design thinking to collaboratively solve our client's most pressing challenges - and do it right the first time. We often work in regulated industries, such as healthcare, financial services, government and education. Our work helps detect diseases, identify kids who need special educational support, applies AI to support complex legal settlements, helps people access mental health services, and so much more.
The Role As a Senior Full Stack Engineer, you will be a key leader across the entire software development lifecycle, from conceptual design to deployment and monitoring. You will be responsible for building robust, scalable backend services using Python and AWS Serverless technologies (Lambda, DynamoDB, SQS, EventBridge), developing event-sourced systems that provide complete state history and auditability. A critical component of this role is a demonstrated fluency with modern AI/ML development tools (e.g., GitHub Copilot, Claude Code, agentic coding tools, large language models). We are looking for candidates who not only use these tools daily but can articulate a clear, thoughtful perspective on their limitations, security implications, and the strategic opportunities they create for the engineering profession.Key Responsibilities
- Lead the design and implementation of highly scalable, cost-efficient serverless backend services using Python and AWS (Lambda, API Gateway, DynamoDB, SQS, 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 best practices for security, logging, monitoring, and infrastructure-as-code (e.g., Terraform or CloudFormation).
- Perform rigorous code reviews, mentor junior and mid-level engineers, and drive technical excellence across the team.
- Actively research, propose, and integrate new technologies and patterns, with a focus on optimizing development velocity and system performance.
- Clearly articulate a professional point of view on how to effectively, securely, and ethically integrate AI tools into the software development lifecycle.
Required
- 5+ years of professional experience in full-stack software development.
- Expert-level proficiency in Python for backend development.
- Strong working proficiency with Node.js.
- Deep practical experience designing, deploying, and maintaining applications using serverless architectures (preferably AWS Lambda, API Gateway, and managed services).
- Hands-on experience with DynamoDB, including single-table design patterns, GSI/LSI strategies, and performance optimization at scale.
- Solid understanding of event sourcing and event-driven architecture patterns, including experience with message brokers or streaming services (e.g., SQS, SNS, EventBridge, or Kinesis).
- Experience with CI/CD pipelines, automated testing, and Infrastructure-as-Code.
- Demonstrated Fluency in AI-Assisted Development : Candidates must be able to demonstrate practical, hands-on experience using AI-powered coding tools to achieve specific, quantifiable productivity gains, while also clearly understanding and mitigating the associated risks (e.g., code quality, security, IP concerns).
- Experience with advanced AWS services (e.g., Kinesis, SageMaker, Step Functions).
- Familiarity with TypeScript for robust front-end or Node.js development.
- Proficiency with a modern front-end framework (e.g., React, Vue, or Angular).
- Experience working in a regulated or compliance-focused environment.
- A Bachelor's or Master's degree in Computer Science or a related technical field.
- Practical AI Tool Usage: Specific examples of AI tools used in professional or personal projects to enhance productivity, solve complex problems, or accelerate development. If you have a preferred agentic tool setup, please share!
- Understanding of AI Limitations: A clear understanding and articulation of the current technical, ethical, and accuracy limitations of modern AI tools in the engineering context.
- Strategic AI Opportunity: A viewpoint on how AI tools create new opportunities for software engineering, such as shifting focus from boilerplate code to complex problem-solving or architectural design.
- Evolution of the Engineering Role: A well-articulated point of view on how your role has already changed (e.g., new workflows, skills, ways of collaborating) due to the introduction of AI and how you expect the role to evolve in the coming years based on your assessment of coming advancements in AI tools and technologies.
- Initial screening email
- A Technical Assessment Interview
- A Panel Interview
Excellent 401(k) plan with annual employer match of 4%
$139k - $201k
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