Overview
We are looking for a fresh graduate (degree or diploma) Data & AI Engineer to join our growing team. This is a hybrid role: about half your time will go into designing, building, and maintaining the data pipelines and infrastructure that our clients rely on, and the other half into building the AI agents and generative AI applications that sit on top of those platforms. You'll get hands-on exposure to the full stack — from raw data ingestion all the way to LLM-powered agents that use that data to take action.
This position plays an important supporting role within our engineering team and is a great launchpad for someone who wants to grow into both data engineering and applied AI engineering.
Key Responsibilities
Data Pipeline & Platform Development
- Design, construct, and maintain scalable data pipelines that handle extraction, transformation, and loading (ETL/ELT) of data from multiple sources.
- Optimise data pipelines for high availability, performance, and data accuracy.
- Automate data workflows to streamline processes and reduce manual intervention.
- Develop, implement, and manage large-scale data processing systems on modern cloud platforms (AWS, Azure, Google Cloud, or similar).
- Design and implement scalable data storage solutions such as data lakes, data warehouses, and databases (e.g., Redshift, Snowflake, BigQuery).
AI & Agentic Systems Development
- Design and build AI agents and generative AI applications that operate on top of the data platforms you help build (e.g., retrieval-augmented generation, autonomous/agentic workflows, LLM-powered internal tools).
- Build and maintain retrieval pipelines — vector databases, embeddings, chunking strategies — that ground AI agents in accurate, up-to-date company data.
- Integrate large language models (via APIs such as OpenAI, Anthropic, or open-source models) into client-facing or internal applications, including tool/function-calling and multi-step agent workflows.
- Prototype, evaluate, and iterate on prompts and agent behaviours; establish basic evaluation and monitoring practices to track AI output quality, cost, and latency.
- Work with orchestration frameworks (e.g., LangChain, LlamaIndex, or similar) to chain data retrieval, reasoning, and action steps.
Collaboration with Stakeholders
- Work closely with data analysts, data scientists, and business teams to understand data and AI use-case requirements and deliver solutions that meet their needs.
- Collaborate with software engineers and IT teams to integrate data and AI systems with existing applications and services.
- Ensure data and AI architecture align with the overall business strategy and technical environment.
Data Governance and Security
- Ensure data quality and reliability through validation, cleansing, and integrity checks throughout the pipeline.
- Enforce data security best practices, including for AI systems (e.g., safe handling of data passed to LLMs, access controls on retrieval sources).
- Implement data governance policies covering metadata, data lineage, and access controls.
Performance Optimisation
- Analyse and optimise query performance on large datasets to ensure fast retrieval and minimise cost.
- Implement monitoring and logging to identify bottlenecks — in both data pipelines and AI agent workflows — and optimise resource usage.
Continuous Improvement
- Stay current with emerging data engineering and AI/agentic technologies.
- Continuously explore ways to improve data architectures, pipelines, and AI-driven processes to enhance efficiency, scalability, and client value.
Qualifications
- Fresh graduate with a Bachelor's degree or Diploma in Computer Science, Information Technology, Data Science, or a related field (or equivalent hands-on experience/projects).
- Strong proficiency in SQL and Python, or another programming language commonly used in data processing.
- Familiarity with cloud-based platforms and services (AWS, Azure, or Google Cloud).
- Exposure to distributed data processing frameworks such as Apache Spark, Hadoop, or Kafka is a plus.
- Familiarity with data warehousing and ETL/ELT tools (e.g., Snowflake, Redshift, Airflow, dbt).
- Understanding of relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) database systems.
- Understanding of data modelling, data architecture, and data governance practices.
- Genuine interest in, or prior exposure to, working with LLMs / generative AI — through coursework, personal projects, internships, or hackathons (e.g., building a chatbot, RAG app, or simple AI agent).
Preferred Skills
- Hands-on experience (even at a project or academic level) with LLM APIs (OpenAI, Anthropic, etc.) or open-source LLMs.
- Exposure to agent orchestration frameworks such as LangChain, LlamaIndex, or similar.
- Familiarity with DevOps/MLOps practices and CI/CD pipelines for data and AI systems.
- Familiarity with containerisation and orchestration tools (Docker, Kubernetes).
- Strong problem-solving skills and comfort working in a fast-paced, client-facing consulting environment.
Benefits
- Competitive salary and bonus structure.
- Opportunities for professional development and certification (cloud and AI/ML certifications supported).
- Direct exposure to real client projects spanning data platforms and cutting-edge generative/agentic AI — a strong foundation for a career at the intersection of data and AI engineering.