Full-Stack AI Engineer
Role details
Job location
Tech stack
Job description
Our client is looking for a hands-on Full Stack AI Engineer who can take full ownership of an AI use case from concept to production. This is more than building models-you'll design, implement, and deploy intelligent systems that deliver meaningful customer value.
Asegúrese de leer detenidamente la información sobre esta oportunidad antes de presentar su candidatura.
You'll work across the entire stack: from architecting analytics infrastructure, to building robust data pipelines, to developing and deploying advanced models. You'll curate high-quality datasets, engineer features, and create AI systems capable of handling complex workflows and adapting to user context.
What You'll Do
- Architect and Implement AI Systems
Design and implement intelligent, production-ready systems that go beyond a single model. You'll choose the right tools for each problem and focus on delivering features that work reliably in real-world conditions.
- Data Preparation and Curation
Prepare, clean, and curate high-quality datasets for modeling. You'll also design and maintain a feature store to ensure consistent, reliable data availability for training and inference.
- Build Robust Data & ML Pipelines
Develop and maintain end-to-end data and machine learning pipelines-from ingestion to deployment and monitoring. This includes building analytics infrastructure using reusable dbt models and designing scalable workflows with Airflow.
- Develop and Deploy Models
Take a hands-on role in model design, training, and evaluation. You'll explore and prototype solutions using a range of neural architectures, including but not limited to LLMs, ensuring performance, reproducibility, and reliability.
- Implement Advanced Retrieval Systems
Design and experiment with Retrieval-Augmented Generation (RAG), Graph RAG, and related methods to enhance information retrieval and reasoning. This includes graph construction, entity linking, and hybrid scoring strategies.
- Enable On-Device Intelligence
Quantize and optimise larger models into efficient versions suitable for on-device and edge processing when appropriate.
Requirements
- End-to-End Ownership
Experience delivering complete AI components-from planning and modeling to deployment, monitoring, and iteration.
- Modeling Expertise
Strong Python skills and deep familiarity with ML frameworks such as Scikit-Learn, TensorFlow, PyTorch, and Hugging Face. You're comfortable designing, evaluating, and prototyping diverse model types.
- MLOps & Data Engineering Proficiency
Hands-on experience with MLOps tools (e.g., MLflow, ZenML), dbt modeling, and working with cloud data warehouses or data lakes.
- Pipeline & Data Skills
Experience building and scheduling pipelines in Airflow. Familiarity with modern data stacks such as Kafka, Spark, and cloud warehouses (BigQuery, Redshift, Snowflake). Ability to define event-level tracking schemas for reliable analytics.
- Problem-Solving & Evaluation
Strong understanding of model behavior and evaluation. Experience developing frameworks for assessing model quality, reliability, hallucination detection, prompt regression, safety scoring, or multi-hop reasoning. Familiarity with RAG, graph-based retrieval, and prompt design. xcskxlj
- Practical, Builder Mindset
A focus on shipping systems that are robust, explainable, and usable by others.
