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