Senior Data Scientist

Website: cc33.co.uk
Job details:

About the Role

We are building AI-powered Voice Bots and intelligent automation products for the contact centre industry. Our stack spans Speech-to-Text (STT), Text-to-Speech (TTS), and Large Language Models (LLMs), with a strong preference for self-hosted, open-source models deployed on our own infrastructure. We're looking for a Data Scientist / Research Scientist who can own the model layer end-to-end — from scouting and benchmarking open-source models to deploying and fine-tuning them on-premises.

 

What You'll Do

• Continuously evaluate and benchmark open-source STT, TTS, and LLM models to identify the best fit for production workloads.
• Recommend and spec out the GPU/hardware requirements for on-premise model deployments, balancing cost, latency, and throughput.
• Fine-tune and adapt foundation models (LLMs, STT, TTS) on domain-specific and multilingual datasets when off-the-shelf performance isn't sufficient.
• Build and deploy traditional ML and Deep Learning models for tasks where a lightweight, purpose-built model outperforms a general-purpose LLM — classification, intent detection, entity extraction, anomaly detection, etc.
• Collaborate closely with the engineering team to integrate models into production voice bot and automation pipelines.
• Stay current with the rapidly evolving open-source AI landscape and bring new techniques, architectures, and tools to the team.

 

Requirements

• Strong foundation in Machine Learning and Deep Learning — hands-on experience building, training, and evaluating models beyond just calling APIs.
• Experience working with open-source LLMs (Llama, Mistral, Qwen, Gemma, etc.) — loading, quantizing, benchmarking, and serving them.
• Familiarity with STT and TTS model ecosystems (Whisper, Coqui, VITS, Piper, etc.) and their performance trade-offs.
• Ability to recommend and justify hardware configurations (GPU type, VRAM, compute requirements) for on-premise model hosting.
• Experience fine-tuning models using techniques like LoRA, QLoRA, or full fine-tuning on custom datasets.
• Proficiency in Python and the core ML/DL toolkit — PyTorch, Hugging Face Transformers, scikit-learn, NumPy, pandas.
• Experience with model serving and inference optimization (vLLM, TGI, ONNX Runtime, TensorRT, or similar).
• Understanding of when to use a large model vs. a small, task-specific model and the ability to make that trade-off pragmatically.
• Strong analytical and problem-solving skills with the ability to read and implement ideas from research papers.

 

Bonus / Good-to-Have (Senior Consideration)

Candidates with the following skills will be considered for a senior position:

• Experience with Python web frameworks such as FastAPI or Flask for building model-serving APIs or backend services.
• Hands-on experience with agentic orchestration frameworks — LangChain, LangGraph, CrewAI, or similar.
• Prior experience hosting and managing LLMs on bare-metal or cloud GPU infrastructure in production (not just experimentation).
• Experience fine-tuning STT and TTS models specifically (e.g., fine-tuning Whisper on domain-specific audio, training custom TTS voices).
• Familiarity with MLOps practices — experiment tracking, model versioning, CI/CD for model pipelines.

 

Click on Apply to know more.