SCRY AI is an innovative AI-driven technology company focused on building intelligent, scalable, and high-performance solutions. We work with modern technologies across Software Engineering, AI/ML, and Data to solve real-world business problems and deliver impactful digital products.
Role Overview
We are looking for a skilled Data Engineer with 2-3 years of experience in building scalable data pipelines, backend services, and data processing systems. The ideal candidate should have strong hands-on experience in Python, SQL, ETL/ELT, API development, and database technologies, with exposure to AI/ML applications, Agentic AI, and cloud or on-premises deployments.
Key Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines for processing structured and unstructured data.
- Build data ingestion and transformation workflows using Python, SQL, and relevant data engineering frameworks.
- Extract, clean, validate, normalize, and transform data from APIs, databases, files, and other sources.
- Design and optimize database schemas, data models, SQL queries, joins, indexes, and aggregations.
- Work with relational databases such as PostgreSQL/MySQL and NoSQL databases where required.
- Develop batch and incremental data processing pipelines with appropriate error handling and recovery mechanisms.
- Implement data quality checks, validation rules, deduplication, and reconciliation to ensure data accuracy and consistency.
- Integrate data pipelines and AI applications with SQL/NoSQL databases, APIs, enterprise systems, and customer-specific data sources.
- Develop Agentic AI and workflow automation solutions using frameworks such as LangChain/LlamaIndex, including tool calling and external system integrations.
- Build and optimize backend services and APIs to support data processing, AI/ML workflows, and enterprise integrations.
- Work directly with customers and stakeholders to understand requirements, troubleshoot issues, demonstrate solutions, and translate business needs into technical implementations.
- Take AI/ML solutions from POC to production, focusing on scalability, reliability, latency, monitoring, and maintainability.
- Deploy and troubleshoot data and AI applications using Docker across cloud, on-premises, and customer environments.
- Implement logging, monitoring, alerting, and performance optimization for data pipelines and production applications.
- Follow engineering best practices using Git, CI/CD, automated testing, and documentation, collaborating with Data Scientists, ML Engineers, Backend Engineers, and business teams.
Key Qualifications
- 2-3 years of experience in Data Engineering, Backend Engineering, ETL development, or a related role.
- Strong proficiency in Python and SQL, including complex queries, joins, CTEs, window functions, and query optimization.
- Hands-on experience building ETL/ELT pipelines, data ingestion workflows, and transformation processes.
- Experience with relational databases, preferably PostgreSQL/MySQL, and database design principles.
- Understanding of data warehousing, dimensional modeling, indexing, partitioning, and incremental data processing.
- Experience integrating REST APIs, databases, CSV/Excel files, JSON, and other data sources.
- Strong experience with FastAPI/Flask, microservices, Docker, and backend architecture; Kubernetes is a plus.
- Experience with AWS/Azure/GCP and cloud or on-premises deployments.
- Knowledge of CI/CD, automated testing, Git, logging, monitoring, production debugging, and AI/ML inference optimization.
- Exposure to LLMs, Agentic AI, tool calling, workflow automation, and frameworks such as LangChain/LlamaIndex.
- Strong problem-solving, communication, customer-facing, and collaboration skills, with the ability to work independently in fast-paced environments.
Good to Have
- Experience with Apache Spark/PySpark, Kafka, or similar distributed data processing and messaging technologies.
- Exposure to cloud data platforms and warehouses such as BigQuery, Redshift, or Azure Data Factory.
- Experience preparing datasets for AI/ML, GenAI, RAG applications, vector databases, or analytics pipelines
