Consultant | AI, Semiconductor, Embedded Systems, Software Engineering, C-Suite

You will work across the entire AI lifecycle, shaping data foundations, developing advanced models, and deploying reliable, production-ready solutions. The focus is on building robust systems that work in real-world environments rather than isolated experiments., * Ownership of end-to-end, production-grade AI systems, from initial concept through deployment, monitoring, and continuous iteration.

• Architecture of intelligent solutions that go beyond single models, selecting appropriate tools, frameworks, and system designs for each use case.
• Design, build, and maintenance of scalable data and machine learning pipelines covering ingestion, transformation, training, deployment, and performance monitoring.
• Preparation, cleaning, and curation of high-quality datasets, alongside the design of feature stores that ensure consistent and reliable data access.
• Development of analytics foundations using reusable dbt models and orchestration of workflows with Airflow.
• Hands-on design, training, evaluation, and deployment of machine learning models, including modern neural architectures and large language models.
• Implementation and experimentation with advanced retrieval and reasoning approaches such as RAG, Graph RAG, hybrid retrieval, graph construction, and entity linking.
• Optimisation and quantisation of models for efficient on-device or edge deployment when required.
• Close collaboration with product, data, and engineering teams to define tracking schemas, event-level data structures, and analytics standards.
• Contribution to the broader AI strategy and scaling of intelligent systems across the platform.

• Proven experience delivering AI solutions end to end, with full ownership from planning and modelling through production and continuous improvement.
• Strong Python skills and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Scikit-Learn, and Hugging Face.
• Practical experience with MLOps tools and practices, as well as data modelling with dbt and working with cloud data warehouses or data lakes.
• Experience building and scheduling pipelines with Airflow and familiarity with modern data stacks including Kafka, Spark, BigQuery, Redshift, or Snowflake.
• Strong understanding of model evaluation, reliability, and behaviour, including hallucination detection, prompt regression, safety scoring, and multi-hop reasoning.
• Solid knowledge of retrieval-augmented systems, graph-based retrieval, and prompt design.
• A pragmatic, builder-focused mindset with a passion for shipping robust, explainable, and production-ready AI systems.