AI Engineer

AI Engineer Own our AI models end-to-end: dataset strategy, training/fine-tuning, evaluation, optimization, and deployment to real hardware in the field.

Below, you will find a complete breakdown of everything required of potential candidates, as well as how to apply Good luck.

You'll turn messy real-world streams into robust, low-latency inference pipelines.

What you'll do Design, train, and fine-tune text/audio/vision models (e.g., DistilRoBERTa, wav2vec2, YOLO) for threat and aggression detection.

Build reproducible training pipelines (HF/ PyTorch/ SpeechBrain), incl.

PEFT/LoRA, adapters, and transfer learning.

Optimize for real-time: quantization, pruning, ONNX/TensorRT, mixed precision, batching, caching.

Ship models to edge & cloud with CI/CD, versioning, and rollback; instrument latency and accuracy SLAs.

Create data pipelines: collection, labeling, augmentation/synthesis, dataset versioning (DVC).

Collaborate with backend/infra on streaming (RTMP/TCP), Pub/Sub, autoscaling, and observability.

Must haves Python wizardry and strong PyTorch .

Hands-on experience with Audio/Video AI
- HuggingFace Transformers, SpeechBrain or torchaudio, one detection/pose stack (YOLO, MediaPipe).

Production MLOps: experiment tracking, model registries, CI/CD, model monitoring.

Comfortable with GCP, containers, and GPUs.

Located in London or ready to relocate .

Nice to haves Real-time inference experience Streaming systems, low-latency audio (VAD/Whisper/wav2vec2), and CV pipelines.

Security/privacy by design (PII handling, encryption, GDPR).

Experience tuning multi-objective metrics (precision/recall trade-offs for safety-critical apps).

The deal: Full-time London-based (or ready to relocate) One-month probation to confirm you have what it takes Career-defining opportunity Competitive London salary share optionsd02fcfcb-6df4-40ee-95cf-3eb059ce3173