Full Stack Engineer

About Us

Testing Mavens is a next-generation QA & Software Testing Services provider headquartered in New Jersey, USA, with offshore facilities in India. We have deep expertise in various spheres like Finance and Advisory, EdTech, Fashion and Pharmacy, etc. We partner with massive global brands to deliver excellent testing experience.

Minimum Requirements:

• Extensive full-stack development experience with a strong emphasis on backend engineering using Node.js, Python, and TypeScript, complemented by solid frontend expertise in Next.js.
• Proven ability to architect, develop, and scale backend systems that integrate complex AI services and data pipelines.
• Deep understanding of microservices architecture, API design principles, and distributed system performance tuning.
• Expertise in building, securing, and maintaining RESTful and GraphQL APIs, with a focus on reliability, observability, and low-latency performance.
• Strong grasp of software security, including authentication, authorization, and data protection for AI and user-facing services.
• Demonstrated success collaborating across AI, product, and DevOps teams, translating technical requirements into scalable, maintainable solutions.
• Familiarity with modern frontend–backend orchestration, state management, and server-side rendering in Next.js.

Preferred Qualifications:

• Advanced experience with cloud-native infrastructure on AWS (ECS, Lambda, S3, RDS, CloudFormation) and containerization using Docker and Kubernetes.
• Proficiency in DevOps and automation pipelines, including CI/CD, GitHub Actions, or GitLab CI, ensuring seamless integration, testing, and deployment workflows.
• Working knowledge of Infrastructure as Code (IaC) using Terraform or AWS CDK, with a focus on scalability and cost optimization.
• Familiarity with OpenAPI / Swagger for API standardization and documentation across services.
• Experience with monitoring, logging, and alerting tools (e.g., Prometheus, Grafana, ELK stack) for maintaining system reliability and performance.
• Deep understanding of data engineering workflows, including interaction with AI pipelines, model APIs, and data streaming (Kafka, SNS/SQS).
• Strong commitment to code quality, test coverage, and secure software practices.