ML Ops Engineer
Posted 4 days ago by Salt
£400 Per day
Outside
Hybrid
London, UK
Summary: The ML Ops/LLM Ops Engineer role involves operationalizing advanced Machine Learning services as part of a digital transformation initiative. The position requires collaboration with various stakeholders to design, deploy, and support production-grade ML services in a fast-paced environment. Candidates should possess strong technical skills and adaptability to evolving Generative AI technologies. The role is hybrid, with potential in-office requirements in East London.
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.
Key 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 (eg 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.
Salary (Rate): £400 per day
City: London
Country: UK
Working Arrangements: hybrid
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: IT
Detailed Description From Employer:
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
- 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 (eg 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