AI Full Stack Data Engineer

Education:

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Bachelor's or Master’s degree in Computer Science, Engineering, or related fields, or equivalent experience.

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Experience:

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o 6+ years of professional experience in software development, with expertise in full stack engineering and backend services.

AI and Machine Learning integration experience.

Proven experience building web applications using frameworks like React, Angular, or Vue for the frontend and Node.js, Python, Java, or similar for backend services.

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Core Skills:

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o Expertise in design patterns, data structures, and algorithms with an emphasis on writing clean, reusable, and maintainable code.

o Strong knowledge and implementation experience with RESTful APIs and/or GraphQL APIs and backend services.

o Proven experience architecting, building, and deploying scalable and reliable systems in distributed environments.

Deep understanding of how to design and implement microservices and related architectural systems.

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Technological Competence:

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o Advanced programming knowledge in Java, Python, Node.js, Scala, or a similar language.

o Familiarity with modern frontend technologies like TypeScript, React, and/or Angular.

o Strong understanding of databases, both relational (PostgreSQL, MySQL) and non-relational (e.g., MongoDB, Cassandra, DynamoDB).

Experience with cloud technologies like GCP, or Azure, and container-based applications using Docker and orchestration tools like Kubernetes.

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Problem-Solving:

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Excellent debugging and problem-solving skills, able to deliver production-ready code for high-scale, global systems.

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Learning Aptitude:

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o Eagerness to learn and adopt new technologies to address complex business challenges.

o Stays current with emerging trends, frameworks, libraries, and developments in the full-stack ecosystem.

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Experience with building and deploying event-driven architectures leveraging messaging queues like Kafka, RabbitMQ, or Pub/Sub.

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Familiarity with DevOps practices, including CI/CD pipelines and tools such as Jenkins, GitHub Actions, or CircleCI.

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Exposure to cloud-native technologies and managed services like Google BigQuery, Lambda or Firebase.

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Strong knowledge of testing frameworks and tools, such as Jest, Cypress, JUnit, or similar, to ensure testable and robust software.

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Some experience with analytics trends, application performance monitoring (APM) tools, or machine learning integration in full-stack systems.

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Understanding of web security principles, authentication standards (e.g., OAuth2, JWT), and performance optimizations for both frontend and backend systems.

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Familiarity with workflow orchestration tools like Apache Airflow or Temporal.