Senior Data & AI Platform Engineer

Key Responsibilities

• Design and develop scalable backend systems
• Define and implement data architecture and database design strategies
• Build and optimize data ingestion and transformation pipelines
• Develop and maintain unified data models across multiple systems
• Design end-to-end data flows and integration strategies
• Collaborate with cross-functional teams to ensure efficient data handling and processing
• Ensure system scalability, performance and reliability

• Work closely with the AI ML team to understand data requirements for model training, feature engineering and pipeline development
• Prepare, validate and deliver clean structured data to support AI ML systems and intelligent workflows
• Consume and integrate outputs from ML models into data pipelines and backend systems
• Maintain awareness of ML concepts to effectively collaborate with AI analytics teams

• Backend & System Design
  • Expertise in system scalability and performance optimization
  • Solid understanding of distributed systems and backend architecture
• Data Engineering Concepts
  • Strong knowledge of data modelling
  • OLTP vs OLAP
  • Strong knowledge of data lake architecture, primary and data warehousing concepts
  • Strong knowledge of REST APIs and API design (FastAPI, Flask, Django)
• Core Platforms & Technologies
  • Strong working knowledge of relational databases (Postgre
    
    SQL, MS SQL Server, MySQL)
  • Non-relational No
    
    SQL databases (Mongo
    
    DB, SQLite, Timescale
    
    DB, Redis)
  • Graph databases (Apache AGE, Neo4j)
  • Comfortable working across multi-database environments – relational, time-series, graph and cache layers in a single architecture
• Good to Have
  • Hands‑on experience with modern data tools such as Kafka, Airbyte, S3, ADLS, Snowflake or similar platforms
  • Experience with cloud platforms (AWS, GCP, Azure)
  • Exposure to real‑time data processing systems
  • Knowledge of ML data pipelines or analytics workflows
  • Experience using Python for data ingestion pipelines, transformation scripts and automation workflows is an advantage

• Passion for experimenting with new tools, frameworks and ideas
• Ability to quickly prototype and iterate on features
• Interest in blending creativity with engineering
• Comfort working in less rigid exploratory development environments
• Strong curiosity and self‑learning mindset

• Use AI‑assisted coding tools like Git Hub Copilot, Cursor, Antigravity, Claude
• Build side projects, prototypes or experimental apps
• Enjoy rapid MVP development or creative coding
• Stay updated with latest trends in AI and developer tools

• Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, or a related field.
• A Master's degree in a relevant field is an advantage.
• 5-7 years of experience.