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