Job Title: ML Ops / LLM Ops Engineer
Location: Hybrid (potentially 1 day per week in east London)
Contract: 6 month Contract
Day Rate: £400 per day (Outside IR35)
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
1. Design and implement tooling and technologies to support ML models and LLMs in production.
2. Deploy, maintain, and optimise machine learning services within a cloud environment (AWS).
3. Recommend and implement prompt management tools and provide expertise in prompt engineering.
4. Introduce and manage observability, monitoring, and evaluation frameworks for ML and AI services.
5. Enable auto-evaluation of prompts and models against domain-specific requirements.
6. Build Python-based microservices, data pipelines, and serverless functions.
7. Collaborate with stakeholders to translate data and AI requirements into scalable solutions.
Essential Experience & Skills
8. 5+ years’ engineering experience, with at least 3 years in ML Ops, Data Engineering, or AI infrastructure.
9. Strong Python engineering skills (Pandas, Numpy, Jupyter, FastAPI, SQLAlchemy).
10. Expertise in AWS services (certification desirable).
11. Proven experience deploying and supporting LLMs in production.
12. Strong understanding of LLM fine-tuning (PyTorch, TensorFlow, Hugging Face Trainer, etc.).
13. Experience with ML tooling ( SageMaker, LangChain/LangSmith, MLflow, Dataiku, DataRobot).
14. Knowledge of embeddings, their applications, and limitations.
15. Hands-on experience in Agile / Lean / XP environments.
16. Excellent communication, problem-solving, and cross-team collaboration skills.
17. Proactive interest in Generative AI trends and best practices.
Desired Skills
18. Experience with chatbots and conversational AI (voice or text).
19. Familiarity with Terraform, Helm, Kubernetes, or Postgres.
20. Exposure to Data Science, NLP, Explainable AI (XAI).
21. Real-world delivery of Generative AI solutions, especially LLM-driven applications.
*Rates depend on experience and client requirements