Lead Machine Learning Engineer

Opus Recruitment Solutions; Remote; GBP 80,000 - 120,000; Full time

Job summary

A technology company is looking for a Lead Machine Learning Engineer to elevate their conversational AI platform. This role entails hands-on leadership, architecting solutions, and mentoring a small engineering team.Candidates should have over 6 years of experience with significant expertise in Python and PyTorch, along with practical insights into LLM solutions. The position emphasizes real-time performance optimizations and safety systems. This is a unique chance to influence the guidelines of cutting-edge AI technology.

Qualifications

• 6+ years in engineering; significant experience with ML/LLM solutions.
• Strong Python/PyTorch skills.
• Understanding of how prompts and architecture influence model behaviour.

Responsibilities

• Architect solutions and mentor engineers.
• Enhance context handling and speed for real-time performance.
• Define strategies for model lifecycle and evaluation.

Skills

Python

PyTorch

LLM solutions

Hugging Face

Job description

Lead Machine Learning Engineer – Conversational AI Platform

Location: Remote / Flexible

Contract Type: Permanent

We’re partnering with a pioneering technology business that’s redefining how humans interact with AI at scale. Their platform powers millions of personalised conversations every day, and they’re looking for a hands‑on technical leader to drive the next evolution of their large language model (LLM) capabilities.

This is a rare opportunity to take ownership of the architecture, optimisation, and deployment of cutting‑edge models in a high‑growth environment. You’ll lead a small, highly skilled team while remaining deeply involved in coding and problem‑solving.

Key Responsibilities

• Lead from the front: Architect solutions, mentor engineers, and contribute production‑level code (Python/PyTorch).
• Optimise the conversational engine: Enhance context handling, retrieval pipelines, and inference speed for real‑time performance.
• Own the model lifecycle: Define strategies for fine‑tuning, RLHF/DPO, and evaluation; oversee data sourcing and curation for improved steerability and diversity.
• Design advanced safety systems: Build classifiers and moderation frameworks that go beyond binary decisions, ensuring compliance without compromising user experience.

What we’re looking for

• Proven delivery at scale: 6+ years in engineering, with significant experience shipping ML/LLM solutions to large audiences.
• Technical depth: Strong Python/PyTorch skills and familiarity with modern LLM stacks (e.g., Hugging Face, vLLM, fine‑tuning pipelines).
• Practical alignment expertise: Understanding of how prompts, sampling, and architecture influence model behaviour.
• Execution mindset: Ability to prioritise shippable solutions over academic perfection.
• Ownership mentality: Committed to performance, safety, and user experience beyond initial deployment.

The environment

• Team: Small, senior engineering group reporting into you, with close collaboration across DevOps and product.
• Culture: High autonomy, rapid decision‑making, and direct access to leadership.
• Tech stack: Python, PyTorch, vLLM, Hugging Face, RAG pipelines, custom moderation systems.