Data Ops + ML Engineer (AI-Native B2B SaaS)

Launch Lab is supporting an early-stage, AI-native B2B SaaS company preparing for a public launch in early 2026. This role is a hybrid Data Ops + ML Engineer position responsible for both data ingestion foundations and AI/ML capability. The platform analyses real customer conversations alongside CRM and campaign data to surface actionable insights for revenue teams. Having successfully delivered a working MVP, the next phase is focused on scaling, improving observability, and laying the foundation for multi-client onboarding. This is a "ground-up" ownership role, setting the standards for data and ML scale-out during a high-growth phase.

About the Product

• The platform is designed to identify customer patterns faster for marketing and commercial teams. It sits at the intersection of B2B, SaaS, LLMs, and sales enablement.
• Al-native platform: Analyses customer conversations and related
  sales/marketing data.
• Modern Stack: Built on a cloud-native stack using Google Vertex AI and serverless workloads.
• Status: A functional MVP is complete and entering scaling and
  enhancement.

Responsibilites

Data Ops

• Extend and automate ingestion connectors including email, transcripts, and conversational tools.
• Maintain standardised metadata and traceability from source to insight.
• Define and evolve data models that support RBAC, analytics, and AI
  retrieval.
• Own data quality validation, schema alignment, and error monitoring.

ML/LLM Engineering

• Improve prompt design, error-handling, and structured output quality.
• Optimise token cost, latency, grounding strategy, and hallucination
  safeguards.
• Introduce evaluation metrics for insight quality and utilisation.
• Partner with engineering for scalable model deployment and lifecycle management.

Must-Have Experience & Skills
Technical Requirements

• Experience: 4+ years in data engineering / ML engineering hybrid teams preferred.
• Languages: Python required.
  Cloud & Infra: Google Cloud Platform, especially Vertex AI and Cloud
  Functions.
• Data Platform: PostgreSQL experience and data modeling capability.
  ETL/Ingestion: Airbyte, Cloud Composer, or similar ingestion orchestration.
• MLOps: API-driven LLM integration (OpenAI, Anthropic, Vertex).
  Soft Skills & Behaviours.
• Ownership mindset: Accountable for outcomes, not just tasks.
• Bias for action: Pragmatism over perfection.
• User empathy: Focuses on AI that feels useful, not just functional.

Success Criteria
First 3-6 Months:

• Ingestion Foundation: Robust, automated ingestion supporting multiple formats and sources.
• Data Quality: Tagging, validation, and metadata enabling meaningful
  downstream AI.
• Insight Consistency: Prompts and model config deliver repeatable, trusted insight patterns.
• Observability: Clear dashboards, alerting, and data lineage controls.

Engagement Details

• Number of Roles: 1
• Target Start Date: February 2026
• Engagement Type: Potential Temp to Permanent hire after 6 months, but primarily looking for perm options
• Location: Fully remote (Core collaboration on UK hours)
• Working Hours: 8h per day
• Core overlap required: 10:00–16:00 London (GMT/BST)

Assessment Process
1. CV & GitHub/Portfolio review.
2. Technical screening (Data + ML scenarios).
3. Live ingestion / prompt engineering exercise.
4. Founder + leadership interview for culture and ownership fit.

What We Offer:

• Full remote with flexible business hours;
• Three weeks of vacation per year (15 working days);
• Paid sick leave per year (5 working days);
• Amazing opportunities for professional growth within a top-notch team of professionals;
• Competitive compensation commensurate with your experience and skills;
• English lessons;
• Co-working compensation;
• An excellent team with a friendly atmosphere.