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