Aws Engineer, DevOps, Machine Learning

1 month ago

England, United Kingdom Methods Analytics Full time

Methods Analytics (MA) is recruiting for an MLOps Engineer to join our team within the Defence Business Unit on a permanent basis.
This role is hybrid, primarily remote, with flexibility required to travel to client sites and our offices in London, Sheffield, and Bristol.

Collaborate with Cross-Functional Teams : Work closely with data scientists, engineers, architects, and other stakeholders to align MLOps solutions with business objectives, explaining complex technical concepts in accessible language for non-technical audiences.
Automate Workflows and Ensure Reproducibility : Write scripts to automate ML workflows and ensure reproducibility of machine learning experiments, enabling consistent and efficient results.
Set Up ML Environments and Deployment Tools : Configure and maintain ML deployment environments using platforms and tools such as Kubernetes, Docker, and cloud platforms (e.g., AWS, Azure), ensuring scalability and reliability.
Build and maintain CI/CD pipelines to streamline model deployment and ensure automated, secure, and reliable model lifecycles from development to production.
Monitor and Maintain Deployed Models : Conduct regular performance reviews and data audits of deployed models, tracking model drift and identifying opportunities for optimisation to enhance performance and reliability.
Security and Vulnerability Management : Participate in threat modelling to identify and assess potential security risks throughout the ML lifecycle. Implement and maintain vulnerability management practices to proactively address security risks, ensuring the integrity and resilience of deployed models and infrastructure.
Troubleshoot and Resolve Issues : Proactively troubleshoot issues related to model performance, data pipelines, and infrastructure, identifying and resolving root causes to maintain stability.
Champion Best Practices and Compliance : Ensure solutions follow best practices in security, scalability, and compliance, particularly aligning with Secure by Design and high-assurance software requirements.
Focus on reusability to maximise development efficiencies, reducing costs across programmes by identifying commonalities and building scalable solutions.
Collaborate on Data Architecture : Work with data architects to ensure the MLOps pipeline integrates seamlessly within the broader data architecture, aligning with governance and compliance standards.

Technical Proficiency in Python and ML Frameworks : Experience with Python and ML frameworks like TensorFlow, PyTorch, or Scikit-Learn, enabling efficient deployment and management of ML models.
Hands-on experience with containerisation and orchestration tools, such as Docker and Kubernetes, to ensure reliable, scalable model deployments.
Proven experience developing and managing CI/CD pipelines using tools like Jenkins, Git, and Terraform, streamlining deployment and automating testing.
Knowledge of Cloud and ML Infrastructure : Experience with cloud platforms (AWS, Azure, or GCP), infrastructure-as-code (IaC) practices, and managing cloud-based ML workflows and resources at scale.
Experience with Threat Modelling and Vulnerability Management : Proven ability to conduct threat modelling exercises to identify security risks and implement vulnerability management practices to ensure robust and secure machine learning systems.
Experience with MLOps Tools and Version Control : Familiarity with tools such as MLflow, DVC, Seldon Core, Metaflow, and Airflow or Prefect, and version control practices for models and datasets to ensure reproducibility, traceability, and compliance across ML workflows.
Experience managing high-performance, low-latency data systems and optimising ML model infrastructure to handle large-scale data in production.
Understanding of Agile Development Methodologies : Familiarity with iterative and agile development methodologies such as SCRUM, contributing to a flexible and responsive development environment.
Knowledge of recent innovations such as GenAI, RAG, and Microsoft Copilot, as well as certifications with leading cloud providers and in areas of data science, AI, and ML.

This role will require you to have or be willing to go through Security Clearance. details of the evidence required to apply may be found on the government website Gov.Methods Analytics (MA) exists to improve society by helping people make better decisions with data. Combining passionate people, sector-specific insight, and technical excellence to provide our customers an end-to-end data service.
We use a collaborative, creative and user centric approach to data to do good and solve difficult problems. We relish difficult problems, and overcome them with innovation, creativity, and technical freedom to help us design optimum solutions. Ethics, privacy, and quality are at the heart of our work, and we will not sacrifice these for outcomes.

We treat data with respect and use it only for the right purpose. Data is a vast topic, but we strive for interactions that are engaging, informative and fun in equal measure. But maintain a steely focus on outcomes and delivering quality products for our customers.
Be part of exciting project work that is making a difference in society
Development access to LinkedIn Learning, a management development programme and training
Wellness 24/7 Confidential employee assistance programme
Social - office parties, pizza Friday and commitment to charitable causes
Time off - 25 days of annual leave a year, plus bank holidays, with the option to buy 5 extra days each year
Volunteering - 2 paid days per year to volunteer in our local communities or within a charity organisation
Pension Salary Exchange Scheme with 4% employer contribution and 5% employee contribution
Discretionary Company Bonus based on company and individual performance
Life Assurance of 4 times base salary
Private Medical Insurance which is non-contributory (spouse and dependants included)
Worldwide Travel Insurance which is non-contributory (spouse and dependants included)

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