ML Ops Engineer

Job Information

• Job Reference: JO-00
• Salary: Up to £400 per day
• Salary per: day
• Job Duration: 6 months
• Job Start Date: 01/10/2025
• Job Industries: Software Engineering
• Job Locations: Greater London
• Job Types: Contract

About the Role

We are seeking an experienced ML Ops / LLM Ops Engineer to join a high-profile digital transformation initiative. This role focuses on operationalising advanced Machine Learning services including Transformers, Large Language Models (LLMs), Automatic Speech Recognition (ASR), and Text-to-Speech (TTS) solutions.

You will work closely with developers, technical leads, product owners, and QA teams to design, deploy, and support production-grade ML services. This is a fast-moving environment where cutting-edge Generative AI technologies are constantly evolving, so adaptability and technical excellence are essential.

Key Responsibilities

• Design and implement tooling and technologies to support ML models and LLMs in production.
• Deploy, maintain, and optimise machine learning services within a cloud environment (AWS).
• Recommend and implement prompt management tools and provide expertise in prompt engineering.
• Introduce and manage observability, monitoring, and evaluation frameworks for ML and AI services.
• Enable auto-evaluation of prompts and models against domain-specific requirements.
• Build Python-based microservices, data pipelines, and serverless functions.
• Collaborate with stakeholders to translate data and AI requirements into scalable solutions.

Essential Experience & Skills

• 5+ years' engineering experience, with at least 3 years in ML Ops, Data Engineering, or AI infrastructure.
• Strong Python engineering skills (Pandas, Numpy, Jupyter, FastAPI, SQLAlchemy).
• Expertise in AWS services (certification desirable).
• Proven experience deploying and supporting LLMs in production.
• Strong understanding of LLM fine-tuning (PyTorch, TensorFlow, Hugging Face Trainer, etc.).
• Experience with ML tooling (e.g. SageMaker, LangChain/LangSmith, MLflow, Dataiku, DataRobot).
• Knowledge of embeddings, their applications, and limitations.
• Hands-on experience in Agile / Lean / XP environments.
• Excellent communication, problem-solving, and cross-team collaboration skills.
• Proactive interest in Generative AI trends and best practices.

Desired Skills

• Experience with chatbots and conversational AI (voice or text).
• Familiarity with Terraform, Helm, Kubernetes, or Postgres.
• Exposure to Data Science, NLP, Explainable AI (XAI).
• Real-world delivery of Generative AI solutions, especially LLM-driven applications.

Rates depend on experience and client requirements

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