Data Ops and ML Engineer – AI-Native B2B SaaS (Remote with UK Team)

We’re hiring a Data Ops & ML Engineer to help scale an AI-native B2B SaaS platform that turns real customer conversations and commercial data into actionable insight for revenue teams.

This is a hands-on, high-ownership role at a critical stage: the product MVP is complete, early users are onboarding, and the focus now is on data reliability, ML quality, and platform scale ahead of a wider launch in 2026.

If you enjoy working across data pipelines, LLMs, and product teams and want your work used directly by customers - this role offers real impact.

The Opportunity

You’ll sit at the intersection of Data Engineering, ML, and Product, owning the foundations that make AI-driven insight trustworthy, explainable, and scalable.

This is not a research-only ML role. You’ll be responsible for:

• ingestion reliability
• data quality and lineage
• prompt and model performance
• observability and governance

You’ll work closely with Fullstack Engineers, Product, DevOps, and the founding team to rapidly iterate and improve real customer outcomes.

What You’ll Be Doing

Data Operations & Ingestion

• Build and extend automated ingestion pipelines (email, transcripts, files, conversational tools)
• Define and maintain metadata, tagging, and traceability from source → insight
• Own data validation, schema alignment, and error monitoring
• Partner with DevOps on observability and diagnostics

ML / LLM Engineering

• Improve prompt design, structured outputs, and error handling
• Reduce hallucination risk and improve grounding and relevance
• Optimise latency, cost, and performance of LLM workflows
• Introduce evaluation metrics for insight quality and usage
• Support scalable deployment of models and future RAG workflows

Product & Collaboration

• Work closely with Product to translate commercial needs into data and ML decisions
• Collaborate with Engineering on architecture and scaling decisions
• Help shape future insight categories, workflows, and onboarding frameworks

What We’re Looking For

Technical Experience

• 4+ years in data engineering, ML engineering, or hybrid Data/ML roles
• Strong Python experience
• Experience with cloud-native data and ML workloads (GCP preferred)
• Hands-on experience with LLM APIs and prompt-driven systems
• PostgreSQL and data modelling experience
• Familiarity with ingestion / orchestration tools (e.g. Airbyte, Composer, similar)
• Experience implementing monitoring, logging, and alerting for data or ML systems

Domain & Ways of Working

• Experience in B2B SaaS environments
• Comfort operating in early-stage or high-ambiguity settings
• Strong understanding of data governance, security, and privacy
• Able to balance speed with reliability and explainability

Soft Skills

• Ownership mindset — accountable for outcomes, not just tasks
• Clear communicator who can translate technical decisions into business impact
• Collaborative partner to Product, Engineering, and Leadership
• Pragmatic, delivery-focused, and user-centred

Location & Working Model

• Remote working with team in UK
• Core overlap required: 10:00–16:00 UK time
• Start: Ideally February 2026
• Compensation aligned with local market median for Data / ML Engineers
• Final package dependent on experience and location

Why This Role?

• Build AI systems that are used directly by customers
• Own data and ML foundations from an early growth phase
• Work in a modern, cloud-native AI environment
• High autonomy and influence over technical direction
• Clear pathway to senior or lead Data / ML responsibility as the team scales