GenAI Engineer

Clarity; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading AI technology firm in Greater London seeks a dedicated AI Systems Engineer. You will design and develop AI solutions that automate customer interactions and enhance workflows. Ideal candidates will have extensive experience in machine learning systems, particularly with AI agents and robust back-end technologies. You will also work collaboratively with engineers and product managers to implement innovative features.

Qualifications

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

Responsibilities

• Build real-time assistants for customer interactions.
• Own reliability and cost of product.
• Collaborate with teams to integrate functionalities.

Skills

Startup hacker mindset

Expert Python

Communication

AI-native dev tools

Tools

GCP

FastAPI

Postgres

Job description

About Clarity

We’re pioneering Agentic AI — systems that don’t just respond, but reason, act, and adapt autonomously in complex workflows. This is about crafting AI Agent Experiences — designing agents that collaborate seamlessly with humans, learn from context, and make every customer interaction faster, smarter, and more empathetic.

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 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