As a Data Engineer, you operate at the intersection of data engineering, software development, and AI-driven technologies. Your role spans the full lifecycle: designing and building platforms, tools, and pipelines that move, transform, and enrich data to generate meaningful and actionable insights.
You tackle a wide range of AI-related challenges — from configuring and optimizing Large Language Models, designing agent and multi-agent architectures, and applying Retrieval-Augmented Generation (RAG) approaches, to developing scalable ETL/ELT processes that power modern data platforms. A recurring theme in your work is orchestrating existing cloud services and APIs into cohesive, reliable, and scalable solutions.
Prompt design is something you approach thoughtfully and creatively. You focus on crafting effective interactions with AI systems, incorporating mechanisms to reduce hallucinations and applying intelligent guardrails. You systematically evaluate outputs to ensure reliability, accuracy, and performance.
Your projects expose you to diverse AI domains: statistical modeling, large-scale language models, generative AI across text, image, audio, and video, as well as natural language processing. The emphasis lies on leveraging and integrating existing AI services and models while designing the supporting infrastructure around them.
You contribute to varied client use cases, such as automated document analysis, event detection in multimedia streams, building agent-based orchestration systems, generating visual content from data, and validating or producing digital content. Wherever AI can create value, you design and implement the end-to-end technical solution.
You collaborate closely with AI specialists and multidisciplinary technical teams on client projects. Your solutions rely on modern cloud platforms, SaaS ecosystems, and programming languages such as TypeScript and Python.
Responsibilities
Solution Design & Architecture
Analyze client challenges related to AI and data, and translate them into structured technical solutions and architectural designs.
Data Pipeline Engineering
Design and implement robust batch and real-time ETL/ELT pipelines.
Modern Data Platforms
Build and maintain data warehouses, data lakes, and scalable analytics environments.
AI Enablement & Integration
Develop ML pipelines and embed AI services within broader data workflows.
Data Governance & Reliability
Ensure data integrity, security standards, and regulatory compliance across the ecosystem.
Scalability & Optimization
Improve processing performance and ensure solutions scale efficiently.
Cloud-Native Data Services
Work with managed cloud services such as AWS Glue, Azure Data Factory, or Google Dataflow.
Observability & Monitoring
Set up monitoring, logging, and alerting mechanisms for data pipelines and AI workloads.
Ideal Profile
You hold a Bachelor’s or Master’s degree with a focus on software engineering or a related discipline.
You bring at least two years of hands-on experience in AI and data engineering, including exposure to different LLM architectures and a solid understanding of their strengths and limitations. You are familiar with modern data architecture principles, data modeling techniques, and large-scale data processing.
You are proficient in data-centric programming languages such as Python and SQL, and have worked with frameworks like Apache Spark, Pandas, or Dask. Experience with AWS or Azure cloud-based data platforms is part of your toolkit.
You have a solid grounding in software engineering practices, including CI/CD workflows, unit testing, Git (and branching strategies), secure coding principles, error handling, asynchronous processing, and streaming data systems.
You are able to translate ideas into architecture diagrams, data flow designs, and structured documentation, while also explaining technical solutions clearly to non-technical stakeholders.
You enjoy exchanging ideas with colleagues, refining concepts collaboratively, and continuously improving solutions through teamwork.
