ML Ops Engineer at Element Materials Technology

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

01 Mar, 26

Salary

0.0

Posted On

01 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

ML Ops, Data Pipeline Orchestration, Data Lake Architecture, Infrastructure as Code, Terraform, Data Extraction, Microsoft Azure, MLOps, Kubernetes, Containerization, ETL/ELT Workflows, Web Scraping, Natural Language Processing, Python, SQL, Spark

Industry

Manufacturing

Description

Overview We are seeking a highly skilled ML Ops Engineer to join our growing data team. This role is critical in designing and implementing robust, scalable, and efficient data systems that power analytics, machine learning models, and business insights. The ideal candidate will have expertise in data pipeline orchestration (e.g., Airflow), data lake and warehouse architecture and development, infrastructure as code (IaC) using Terraform, and data extraction from both structured and unstructured data sources (e.g. websites). Knowledge using the Microsoft Azure ecosystem, MLOps, Kubernetes, and other modern data engineering practices. Responsibilities Data Architecture & Development: Leverage best practices in schema design, partitioning, and optimisation for efficient storage and retrieval. Build and maintain data models to support analytics and machine learning workflows. Design and implement scalable, secure, and high-performance data lake and data warehouse solutions. Pipeline Orchestration: Develop, monitor, and optimize ETL/ELT workflows using Apache Airflow. Ensure data pipelines are robust, error-tolerant, and scalable for real-time and batch processing. Data Scraping & Unstructured Data Processing: Develop and maintain scalable web scraping solutions to collect data from diverse sources, including APIs, websites, and other unstructured data sources. Extract, clean, and transform unstructured data such as text, images, and log files into structured formats suitable for analysis. Use tools and frameworks like BeautifulSoup, Scrapy, or Selenium for web scraping, and natural language processing (NLP) techniques for text processing. Cloud Integration: Design and implement cloud-native data solutions with Microsoft Azure. Optimize costs and performance of cloud-based data solutions. Infrastructure as Code (IaC): Use Terraform to automate the provisioning and management of cloud infrastructure. Define reusable and modular Terraform configurations to support scalable deployment of resources. MLOps: Collaborate with data scientists and machine learning engineers to operationalise machine learning models. Responsible for deploying LLMs and debugging failures in production environments to detect change in data and model behaviour. Implement CI/CD pipelines for machine learning workflows, ensuring efficient model deployment and monitoring. Containerisation and Orchestration: Utilize Kubernetes and containerisation technologies (e.g., Docker) to deploy scalable, fault-tolerant data processing systems. Manage infrastructure and resource allocation for containerised data applications. Cross-Functional Collaboration: Work closely with stakeholders, including data scientists, software engineers, and business analysts, to align technical solutions with business needs. Mentor junior engineers and foster a culture of continuous learning within the team. Skills / Qualifications Education: Bachelor's/Master’s/PhD degree in Computer Science, Engineering, or a related field; or equivalent professional experience. Experience: 5+ years of experience in data engineering or a related field. Strong expertise in data pipeline orchestration tools such as Apache Airflow. Proven track record of designing and implementing data lakes and warehouses (experience with Azure is a plus). Demonstrated experience with Terraform for infrastructure provisioning and management. Solid understanding of MLOps practices, including model training, deployment, and monitoring. Hands-on experience with Kubernetes and containerised environments. Knowledge and experience of working with Large Langauge Models. Interest in working alongside applied researchers and data scientists. Technical Skills: Proficiency in programming languages such as Python & SQL. Experience with distributed computing frameworks such as Spark. Familiarity with version control systems (e.g., Git) and CI/CD pipelines. Soft Skills: Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment. Excellent communication and documentation skills. Strong analytical mindset with attention to detail. #LI-DJ1 Diversity Statement At Element, we always take pride in putting our people first. We are an equal opportunity employer that recognizes diversity and inclusion as fundamental to our Vision of becoming “the world’s most trusted testing partner”. All suitably qualified candidates will receive consideration for employment on the basis of objective work related criteria and without regard for the following: age, disability, ethnic origin, gender, marital status, race, religion, responsibility of dependents, sexual orientation, or gender identity or other characteristics in accordance with the applicable governing laws or other characteristics in accordance with the applicable governing laws. The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60-1.35(c) “If you need an accommodation filling out an application, or applying to a job, please email Recruitment@element.com”

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Responsibilities

The ML Ops Engineer will design and implement scalable data systems that support analytics and machine learning models. They will also develop and optimize data pipelines and collaborate with data scientists to operationalize machine learning models.