Senior Data & AI Platform Engineer

We are looking for a Senior Data & AI Platform Engineer with strong expertise in data architecture, database design, and scalable system development. The ideal candidate should have a deep understanding of data engineering concepts, modern data platforms, and the ability to design robust, scalable backend systems and data pipelines.

Key responsibilities:

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

Machine Learning & AI Integration

• 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

Core Technical Skills

• Backend & System Design

• Expertise in system scalability and performance optimization

• Solid understanding of distributed systems and backend architecture

• Data Engineering Concepts (IMPORTANT)

• 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 (Required)

Strong working knowledge of:

• Relational databases - strong hands-on experience required (e.g. PostgreSQL, MS SQL Server, MySQL)

• Non-relational / NoSQL databases - working knowledge required (e.g. MongoDB, SQLite, TimescaleDB, Redis)

• Graph databases - understanding of graph data models (e.g. Apache AGE, Neo4j concepts)

• 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

Data Ingestion & Processing

Ability to design and implement:

• Data ingestion pipelines

• Data transformation layers (ETL/ELT)

• Unified and scalable data models

• System Thinking & Architecture

Ability to:

• Design scalable backend architectures

• Define and manage data flow across systems

• Guide integration strategies between multiple services and platforms

Good to Have

• 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

What We’re Looking For

• 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

Bonus Points If You

• Use AI-assisted coding tools (like GitHub 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

Requirements (Qualifications/Experience/Competencies)

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

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