AI Data Engineer

Role Overview We are hiring a hands-on engineer with strong capabilities in data pipelines and applied AI (GenAI) to build and support our AI-driven platform and Datalake platform. Key Responsibilities Own the architecture, operation, and continuous improvement of the enterprise Data Lake and AI Platform. Design, implement, and maintain scalable data pipelines using AWS Glue, Apache Iceberg, Redshift, and related cloud-native technologies. Ensure data quality, governance, lineage, observability, security, and platform reliability. Establish standards and best practices for data ingestion, transformation, storage, and consumption. Design, build, and deploy AI Agents, AI Advisors, and GenAI-powered business solutions. Develop Retrieval-Augmented Generation (RAG)architectures leveraging enterprise knowledge and data assets. Design multi-agent workflows to automate business processes and improve user productivity. Evaluate emerging AI technologies and identify opportunities to enhance AI capabilities across the organization. Core Skills (Must-Have) 1. Data Engineering Fundamentals Strong hands-on experience in ETL/ELT pipeline development Proficient in data transformation, cleaning, and modeling Solid experience with SQL and working with large datasets Familiar with Airflow, AWS Glue, S3, Redshift, Lambda Understanding of data quality, lineage, and reliability concepts 2. Programming & Backend Development Strong proficiency in Python (preferred) or similar backend language Experience building RESTful APIs and backend services Ability to write clean, maintainable, production-grade code 3. GenAI / LLM Capabilities Hands-on experience working with LLMs (e.g. OpenAI, Claude, or QWEN) Understanding of Retrieval-Augmented Generation (RAG) architecture Experience with embeddings, vector databases, and prompt orchestration Ability to connect enterprise data with LLMs in a secure and scalable way 4. Data Storage & Systems Experience with relational databases (e.g. MySQL, PostgreSQL) Familiarity with NoSQL / document stores Understanding of data lake / warehouse concepts 5. Deployment & Platform Skills Experience with Docker and containerization Basic familiarity with Kubernetes / AWS / OpenShift or similar platforms Understanding of CI/CD practices for backend or data applications Good-to-Have Skills Experience with streaming data (Kafka or equivalent) Exposure to machine learning workflows Experience with API gateways, authentication, and security practices Familiarity with cloud platforms (AWS) Prior experience in financial services / trading systems Key Attributes Able to operate as a hybrid engineer across data and AI domains Strong problem-solving and system design thinking Comfortable working in ambiguous, fast-moving environments Focus on delivering working solutions, not just prototypes Scope (High-Level) Build and maintain data pipelines Enable AI/GenAI use cases (e.g. AI Advisor, Research Chatbot etc.) Integrate AI capabilities into applications and services