Data Science Tech Lead: GenAI

Posted 1 day 16 hours ago by Clarity

£85,000 - £120,000 Annual
Permanent
Full Time
Other
London, United Kingdom
Job Description

Data Science & GenAI Tech Lead - AI Agents, Structured Insights & Detection

About Clarity

Clarity is redefining customer experience with AI. Our mission is to empower businesses to deliver faster, smarter, and more human service interactions. By combining cutting edge AI with intuitive design, we enable customer service teams to operate more efficiently while providing customers with seamless, personalized experiences.

We are trusted by industry leaders like OpenAI, GrubHub, STC and Tabby who rely on us to deliver real impact. Our investors include Prosus Ventures, STV AI Fund (backed by Google) and angels from Open AI and Google. With a 25% month on month growth rate and over 300% net revenue retention, this is a unique opportunity to join a hyper growth AI company and redefine an industry.

What you'll actually do 50% Build - design & ship

  • Agentic AI for CX: Real time assistants that listen to calls/chats, retrieve from customer KBs, and draft responses with human in the loop controls.

  • Structured extraction: Schema driven pipelines over unstructured text (and other modalities) using retrieval, tool use, and robust LLM prompting.

  • Hybrid anomaly detection: Blend classical time series methods (e.g., decomposition, change point, forecasting) with LLM aware, contextful detectors for seasonality, spikes, step changes, and drift.

  • Novelty discovery: Embedding based clustering and drift, topic surfacing, LLM summarization of emerging themes with deduplication and evidence links.

  • Alerting & scoring: Severity/impact ranking, de noising, suppression/cool downs, routing, and feedback loops.

25% Architect & scale

  • Own reliability, latency, and cost. Design online/offline eval harnesses, canaries, and SLAs; operate GPUs/accelerators where needed.

  • Stand up and harden RAG pipelines (indexing, retrieval policies, grounding, guardrails) and agent frameworks.

  • Take basic infra ownership on GCP (or AWS/Azure): networking, autoscaling, CI/CD, IaC, observability, and cost tuning.

  • Participate in on call for your area and drive root cause analysis with crisp follow ups.

15% Collaborate

  • Pair with back end & front end to wire extractors/detectors and agents into ticketing, voice, and analytics stacks (APIs, webhooks, real time streams).

  • Partner with PMs/CX to evolve taxonomies, schemas, and guardrails; translate business problems into shipped ML features.

10% Align & showcase

  • Gather requirements from CX and product leads, demo new capabilities to execs & customers, and document impact with precision/recall, alert quality, latency, and cost metrics.

What makes you a great fit

  • Startup hacker mindset: You self start from zero, respect no silos, and carry work from prototype to production. ️

  • AI native dev tools are your daily drivers: Cursor, v0, Claude Code (or similar).

  • 7-10 years building production ML/back end systems; 2+ years leading while coding.

  • Expert Python; strong back end chops (e.g., FastAPI, gRPC, Postgres, pub/sub/streams).

  • Agents & RAG: Fluency with at least one agent framework (ADK preferred). Proven track record shipping AI agents and building RAG pipelines.

  • LLM + DS depth: Prompting/tooling, retrieval design, LLM evals; hands on with time series analysis (forecasting, change point, drift).

  • Cloud & ops: Basic infra ownership on GCP (or AWS/Azure): networking, autoscaling, CI/CD, IaC, observability, and cost control.

  • Communication: You explain results clearly, align stakeholders, and write crisp docs.

Bonus points

  • DevOps wizardry; GPU/accelerator experience.

  • Multimodal pipelines (text + voice + screenshots).

  • Prior experience in contact center/CX analytics or novelty/anomaly systems.

  • Founder or founding engineer experience