AI Data Engineer

Role OverviewWe 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 ResponsibilitiesOwn 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 FundamentalsStrong hands-on experience in ETL/ELT pipeline developmentProficient in data transformation, cleaning, and modelingSolid experience with SQL and working with large datasetsFamiliar with Airflow, AWS Glue, S3, Redshift, LambdaUnderstanding of data quality, lineage, and reliability concepts2. Programming & Backend DevelopmentStrong proficiency in Python (preferred) or similar backend languageExperience building RESTful APIs and backend servicesAbility to write clean, maintainable, production-grade code3. GenAI / LLM CapabilitiesHands-on experience working with LLMs (e.g. OpenAI, Claude, or QWEN)Understanding of Retrieval-Augmented Generation (RAG) architectureExperience with embeddings, vector databases, and prompt orchestrationAbility to connect enterprise data with LLMs in a secure and scalable way4. Data Storage & SystemsExperience with relational databases (e.g. MySQL, PostgreSQL)Familiarity with NoSQL / document storesUnderstanding of data lake / warehouse concepts5. Deployment & Platform SkillsExperience with Docker and containerizationBasic familiarity with Kubernetes / AWS / OpenShift or similar platformsUnderstanding of CI/CD practices for backend or data applicationsGood-to-Have SkillsExperience with streaming data (Kafka or equivalent)Exposure to machine learning workflowsExperience with API gateways, authentication, and security practicesFamiliarity with cloud platforms (AWS)Prior experience in financial services / trading systemsKey AttributesAble to operate as a hybrid engineer across data and AI domainsStrong problem-solving and system design thinkingComfortable working in ambiguous, fast-moving environmentsFocus on delivering working solutions, not just prototypesScope (High-Level)Build and maintain data pipelinesEnable AI/GenAI use cases (e.g. AI Advisor, Research Chatbot etc.)Integrate AI capabilities into applications and services