ML Ops Engineer

Key Skills

Analytical and Problem solving, AWS, Azure, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Docker, Effective communication skills, Google Cloud Platform (GCP), Kubernetes-K8s, Large Language Models - LLMs, Machine learning techniques, MLOps tools, Optimization, PyTorch, TensorFlow

Job Description

Blenheim Chalcot is the UK’s leading digital venture builder. We invest more than just funds, we invest our knowledge and experience, our ideas and infrastructure. Our ventures are at the forefront of a multitude of industries being disrupted digitally, including FinTech, EdTech, GovTech, Media, Sport, Charity and more. Ventures we have built range from ClearScore to Agilisys and even the Rajasthan Royals IPL cricket team. All our 20+ portfolio companies have been incubated and launched by us and now have total sales of over £0.5bn and more than 3,000 employees. Our assets under management stand at greater than £1.5bn.

Role Overview

As a Machine Learning Operations Engineer, you will play a pivotal role in developing, deploying, and managing machine learning models and large language model (LLM) systems across our diverse portfolio of companies. This position calls for a blend of technical expertise, a passion for innovation, and the ability to work alongside entrepreneurs to drive growth and transform industries.

Responsibilities

• Design, build, and maintain efficient, reliable, and scalable ML and LLM operations infrastructure.
• Implement robust ML model lifecycle management practices, including development, testing, deployment, and monitoring.
• Work closely with data scientists and ML engineers to facilitate the seamless transition of models from experimentation to production.
• Ensure the highest levels of security and compliance are maintained in all ML and LLM operations.
• Optimize model performance and resource utilization to meet the demands of rapidly scaling ventures.
• Stay abreast of the latest developments in ML and LLM technologies and methodologies, integrating these innovations to enhance operational efficiency and model effectiveness.

Must have

• Proven experience in ML and LLM operations, with a strong understanding of ML model lifecycle management.
• Proficiency in Python, and experience with ML frameworks like TensorFlow or PyTorch.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities, with a knack for working effectively in a dynamic, team-oriented environment.
• Familiarity with CI/CD pipelines, automation tools, and ML monitoring solutions.
• Knowledge of data engineering principles and practices is highly desirable.
• A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• Minimum of 3 years of relevant experience in machine learning operations.

Nice to have

• Minimum of 5 years of relevant experience in machine learning operations, with a preference for candidates who have experience managing large language models.
• Experience with cloud computing platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).