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.
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.
Our Ideal Candidate
We’re looking for a practical, value-driven AI engineer. You should have:
● 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.
● Practical, Builder Mindset
A focus on shipping systems that are robust, explainable, and usable by others.