<p><strong>Senior Data Scientist / Applied AI Engineer</strong></p><p><strong>Hybrid (3 days onsite per week)</strong></p><p><strong>120-177K base salary + bonus</strong></p><p><br></p><p>We’re partnering with a major enterprise organization undergoing significant investment in AI and data capabilities. This role sits within a central AI function focused on building production-grade machine learning and generative AI solutions that improve customer experience, operational efficiency, and decision intelligence across the business.</p><p><br></p><p>You’ll work on real, deployed AI systems - collaborating closely with product, engineering, and business stakeholders to design, build, and scale intelligent applications.</p><p><br></p><p>What You’ll Be Doing</p><ul><li>Deliver AI and machine learning solutions that solve real operational and customer-facing challenges</li><li>Contribute across the full model lifecycle — from data exploration and feature engineering through to deployment, monitoring, and iteration</li><li>Build and productionize ML and GenAI solutions using modern cloud and data platforms</li><li>Design and evaluate intelligent automation solutions using LLMs, retrieval systems, and agent-style architectures</li><li>Implement and optimize RAG pipelines, including embeddings, vector search, retrieval tuning, and prompt orchestration</li><li>Expose AI capabilities through APIs, internal tools, and workflow applications used by business teams</li><li>Build rapid prototypes and lightweight interfaces to support validation and adoption</li><li>Follow best practices around model governance, testing, monitoring, and CI/CD in collaboration with platform and MLOps teams</li></ul><p><br></p><p>What We’re Looking For</p><ul><li>Advanced degree in Computer Science, Engineering, Mathematics, Statistics, or similar quantitative field</li><li>7+ years applying data science, machine learning, or applied AI in production environments</li><li>Strong Python and SQL skills</li><li>Solid understanding of software engineering fundamentals (version control, testing, logging, deployment workflows)</li><li>Experience working with modern cloud and data platforms (e.g. AWS-based ML tooling, enterprise data warehouses, distributed compute platforms)</li><li>Practical exposure to LLMs, RAG architectures, or agent-based systems</li><li>Strong grounding in core ML concepts including feature engineering, model evaluation, and classical ML approaches (e.g. tree-based models, supervised/unsupervised learning)</li><li>Ability to communicate technical work clearly to non-technical stakeholders and influence decision making</li></ul><p><br></p><p>If you're interested, please apply now!</p><p></p>
