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

Join a forward-thinking team where you will take full ownership of AI solutions from concept through to production. This role is ideal for a hands-on Full-Stack AI Engineer who enjoys building intelligent, scalable systems that deliver real customer value across data, machine learning, and infrastructure. 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. Responsibilities 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. What you bring

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.