AI Full Stack Engineer – Integration & GenAI
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AI Full Stack Engineer – Integration & GenAIResponsibilities
- Analyze business and technical requirements and translate them into end-to-end system, data flow, and integration designs.
- Design and develop REST APIs, SFTP, file-based interfaces, and batch integrations across upstream and downstream enterprise systems.
- Develop Python scripts and backend services for data extraction, transformation, validation, automation, and system integration.
- Design and implement reliable data pipelines and data movement across enterprise platforms, Data Lake, and downstream applications.
- Perform data mapping, transformation, aggregation, reconciliation, and data quality validation to ensure accurate data delivery.
- Support GenAI and RAG use cases through document ingestion, data preparation, enrichment, transformation, and AI-ready data pipelines.
- Develop and maintain application components using Python, SQL, Celery, HTML, Jinja, and TypeScript.
- Support SIT, UAT, deployment, production troubleshooting, incident resolution, and application enhancements.
- Work with Docker, Kubernetes/OpenShift, CI/CD, Git, Jenkins, Control-M, and monitoring tools to ensure reliable application delivery.
- Collaborate with business, application, data, infrastructure, security, and architecture teams to resolve issues and deliver scalable solutions.
- Minimum of 3 years relevant experience in System Analysis, Integration Engineering, Data Engineering, Full Stack Engineering, or Technical Delivery roles.
- Strong hands-on experience in Python and SQL for development, data handling, automation, integration, and troubleshooting.
- Practical experience developing and integrating REST APIs, SFTP, batch processes, and file-based interfaces.
- Strong understanding of data mapping, transformation, aggregation, reconciliation, data quality, and enterprise data flows.
- Experience with enterprise data platforms such as Informatica, Cloudera, Data Lake, or similar technologies.
- Good knowledge of Celery, Docker, Kubernetes/OpenShift, CI/CD, Git, Jenkins, and Bash.
- Working knowledge of HTML, Jinja, and TypeScript with exposure to application/UI development.
- Experience supporting SIT/UAT, production deployments, incident management, debugging, and application enhancements.
- Exposure to GenAI concepts including document ingestion, RAG, embeddings, data preparation, and AI-enabled workflows; ML model development is not mandatory.
- Strong stakeholder management skills, with the ability to understand complex enterprise ecosystems.