Data Scientist building predictive weather models and commercial data products for Pelmorex, a weather intelligence company. Developing scalable machine learning solutions for B2B forecasting, advertising, and enterprise decision - making.
About the role
- AI Engineer building production LLM agents, data pipelines, and analytics tooling for Thomson Reuters’ tax, audit, and accounting products. Monitoring agent quality and supporting product decisions.
Responsibilities
- Build and integrate pre-trained models, LLM APIs, tool-calling, and MCP-style integrations into analytics products
- Refactor experimental notebooks and prototypes into production-ready components with error handling, guardrails, telemetry, and logging
- Prepare, clean, and structure datasets used by AI agents
- Build and maintain data pipelines and contribute to product data modeling and exposure for LLM-based reasoning
- Work within shared repositories, reusable components, and context and data-access standards
- Register shipped components for discoverability and reuse
- Apply validation sampling, source traceability, prompt and model version documentation, and testing for non-deterministic model behavior
- Diagnose production failures and quality drift, conduct root cause analysis, and update context, prompts, and logic as data or business rules change
- Contribute to proactive intelligence systems, natural language interfaces, semantic layers, and feedback loops
- Interpret business, functional, and technical requirements and translate ambiguous business needs into agent designs alongside senior engineers
- Participate in planning, code reviews, team ceremonies, documentation, and status updates
- Report to the Director, Product Analytics
Requirements
- Bachelor’s degree in Computer Science, Data Science, or a related field
- 3+ years in software engineering, data engineering, data science, or analytics
- Hands-on exposure to building or contributing to LLM-based agents or AI-native systems
- Demonstrated personal investment in AI through independent experimentation and projects
- Proficiency in Python and strong SQL
- Solid grounding in programming concepts, design patterns, SDLC principles, and unit testing
- Exposure to RAG pipelines, multi-step reasoning agents, tool-calling, or MCP-style integrations
- Exposure to vector databases, embeddings, semantic search, and retrieval pipelines
- Experience refactoring experimental or research code into production-ready components
- Familiarity with MLOps and LLMOps practices, including testing, evaluation, and monitoring
- Clear written and verbal communication, including explaining technical concepts to non-technical stakeholders
- Preferred: product sense, product-team or SaaS experience
- Preferred: experiment design, statistical inference, A/B testing, or applied ML
- Preferred: hands-on LLM API integration, including prompt and response handling and cost and safety considerations
- Preferred: hybrid or advanced retrieval, agent harness and orchestration optimization, context engineering, guardrails, and observability
- Preferred: modern data stack experience such as Snowflake or Databricks
- Preferred: cloud computing and containerization foundations such as AWS, Azure, GCP, or Docker
- Preferred: awareness of AI governance and compliance considerations
Benefits
- Flexible hybrid working environment
- Flexible work arrangements, including work from anywhere for up to 8 weeks per year
- Continuous learning and skill development through Grow My Way programming
- Flexible vacation
- Two company-wide Mental Health Days off
- Access to the Headspace app
- Retirement savings
- Tuition reimbursement
- Employee incentive programs
- Resources for mental, physical, and financial wellbeing
- Two paid volunteer days off annually
- Opportunities for pro-bono consulting projects and ESG initiatives
- Annual Bonus based on a combination of enterprise and individual performance (eligibility may apply)
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