Overview

Data Science and GenAI Tech Lead — AI Agents, Structured Insights and DetectionLocation:

London, hybridAnecdote we’re on a mission to make customer experience delightful for everyone involved. Think a real-time copilot that listens to live calls and chats, reads our customers’ knowledge bases, and drafts high-quality replies for human agents - while also turning messy, multi-channel feedback into trustworthy

structured insights ,

anomaly detection , and

novelty discovery .As the Tech Lead, you’ll own the technical vision and turn requirements into a live, reliable product used by brands like

Grubhub, Booking.com, Dropbox, Uber, Careem, and Fubo . You’ll collaborate directly with engineers, other tech leads, directors, and the CTO to evolve ambitious prototypes into a rock-solid, scalable platform.

What you’ll actually do50% Build — design and 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 and scoring:

Severity/impact ranking, de-noising, suppression/cool-downs, routing, and feedback loops.

25% Architect and 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 and 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 and showcase

Gather requirements from CX and product leads, demo new capabilities to execs and 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 and 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 and 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

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Research, Analyst, and Information Technology

Industries Software Development

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