DEEPREC.AI

Role Description We're partnering with an AI-driven technology company looking to hire an AI Knowledge Graph/ Ontology Lead to build and scale the knowledge graph layer underpinning a new generation of intelligent enterprise products. This is a senior, hands-on leadership role for someone who has real experience designing and shipping production knowledge graphs, not just conceptual ontology work. You'll be responsible for shaping a graph and ontology platform that powers: - AI retrieval and RAG workflows - Entity linkage and reasoning systems - Cross-domain and temporal knowledge modeling - Regulatory and compliance intelligence products - Agentic AI applications The role combines architecture, hands-on engineering, and team leadership. You'll work closely with AI/ML engineers to turn complex, unstructured information into structured, queryable intelligence that directly feeds live AI systems. Qualifications - Strong Neo4j and Cypher expertise in production environments - Deep ontology / taxonomy modeling experience - Python engineering skills - Knowledge graph integration with LLMs, RAG, or vector search systems - Experience balancing formal semantics with pragmatic implementation - Ability to lead technically while remaining hands-on Requirements - Additional experience in areas such as RDF/OWL, inference engines, entity resolution, legal/regulatory data, ESG, healthcare, or pharmaceutical domains would be highly valuable. Benefits - A highly technical AI environment - Significant ownership and architectural influence - Fast decision-making and direct access to leadership - Strong adoption of AI-assisted development tooling - Flexible working arrangements and competitive compensation Company Description Ideal for someone excited by the challenge of building the intelligence layer behind complex AI products at scale. Please apply for more details.

Role Description We're working with one of Europe's most exciting robotics startups, building intelligent robotic systems that are already being deployed into real manufacturing environments. Following significant growth, they're looking for an experienced AI Team Lead to take ownership of a multidisciplinary AI function focused on computer vision, perception and machine learning for real-world robotics. You'll lead a team of around nine AI engineers while remaining close to the technology: - Leading, mentoring and developing a growing team of AI engineers - Driving technical direction across perception, computer vision and machine learning - Conducting code reviews and providing technical guidance across the team - Hiring, performance managing and coaching engineers - Working closely with robotics and software teams to deliver production-ready AI systems - Defining engineering standards, best practices and development processes - Supporting the deployment of AI models onto real robotic hardware - Helping shape the long-term AI roadmap alongside senior engineering leadership Qualifications - Previous experience leading an AI, Machine Learning or Computer Vision engineering team - A strong background in Computer Vision and deep learning - Experience building production ML systems rather than purely research projects - Strong Python and PyTorch experience - Experience working with multimodal data and large-scale datasets - Exposure to robotics, embedded systems or hardware-focused AI applications - Confidence reviewing code and supporting engineers with technical challenges - Experience hiring, mentoring and managing performance within engineering teams Requirements - Bonus points for Robotics or embodied AI experience - 3D perception or sensor fusion - ROS2, Docker or Kubernetes - NVIDIA edge platforms - Synthetic data or simulation - Cloud infrastructure and MLOps tooling Benefits This is an opportunity to shape the AI capability of a rapidly growing robotics company solving genuinely difficult engineering problems. You'll join a business with real customers, production deployments and ambitious growth plans, working alongside an experienced leadership team with the autonomy to influence both technology and team direction. If you're an experienced technical leader who still enjoys getting into the code and wants to build AI that operates on real robots rather than prototypes, we'd love to hear from you. Interested? Apply now for a confidential discussion to learn more about the company, technology and opportunity.

Role Description We are looking for an experienced Data Engineer to join a growing data and AI team, helping to build modern data solutions that support advanced analytics and machine learning. This role centres on building robust data ingestion pipelines within Snowflake and delivering predictive analytics using Snowflake Cortex. While the majority of machine learning will leverage Snowflake's managed ML capabilities, there will also be opportunities to develop bespoke Python models where business requirements extend beyond native functionality. You will play a key role in designing scalable data engineering solutions, preparing high-quality datasets, and ensuring production-ready machine learning outputs are delivered to downstream applications. - Design, build and maintain scalable data ingestion pipelines into Snowflake from structured and time-series data sources. - Develop predictive analytics solutions using Snowflake Cortex, including forecasting, anomaly detection and classification. - Prepare machine learning datasets through feature engineering, windowed aggregations and time-series transformations. - Develop custom machine learning models using Python and Snowpark (including scikit-learn, XGBoost and LightGBM) where native Snowflake capabilities are not suitable. - Integrate machine learning outputs into downstream applications and analytics platforms. - Monitor and maintain production data pipelines and machine learning solutions, including data quality, model refreshes and performance monitoring. - Optimise Snowflake performance and ensure data engineering best practices are followed. Qualifications - Strong commercial experience with Snowflake, including SQL, Streams, Tasks, Snowpipe and performance optimisation. - Experience using Snowflake Cortex ML capabilities for forecasting, anomaly detection and classification. - Strong Python development skills, including Snowpark. - Experience building data pipelines and preparing datasets for machine learning. - Knowledge of time-series data processing and feature engineering techniques. - Experience developing machine learning models using libraries such as scikit-learn, XGBoost or LightGBM. - Understanding of production monitoring, data quality and model lifecycle management. Requirements - Experience with Snowflake ML features such as Model Registry and Feature Store. - Knowledge of Azure Data Lake, Microsoft Fabric or other modern cloud data platforms. - Familiarity with Snowflake AI capabilities such as AISQL, Cortex Search or related technologies.