Data Science Tech Lead : GenAI

Clarity; Greater London; On-site; GBP 85,000 - 120,000; Full time

Job summary

A leading AI technology company in Greater London is seeking a Data Science & GenAI Tech Lead to spearhead the development of AI-driven customer experience solutions. You will design and implement real-time AI assistants and manage structured data extraction processes while collaborating with various teams. Candidates should have a strong background in Python, production ML systems, and cloud infrastructure management. This role offers the chance to redefine customer service with innovative technology.

Qualifications

• 7–10 years of experience building production ML/back-end systems.
• 2+ years leading while actively coding.
• Proven experience shipping AI agents and building RAG pipelines.

Responsibilities

• Design and ship real-time AI assistants.
• Develop schema-driven pipelines over unstructured text.
• Participate in on-call support and root-cause analysis.

Skills

Expert Python

ML/back-end systems

Communication

AI-native developer tools

Cloud infrastructure ownership

Tools

FastAPI

GCP

Postgres

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