We are a technology company combining advanced AI with human intelligence and expertise to tackle harmful and problematic online content at scale. We want to provide everyone, from individual citizens to national governments, with the tools they need to identify and disarm damaging and misleading information.
Since we were founded in 2017, Logically has been working to protect democratic debate and process, and provide access to trustworthy information. This has led us to create a suite of products and services to reduce and eventually eliminate the harm caused by the spread of misinformation and targeted disinformation campaigns.
We are an award-winning international team of over 200 data scientists, engineers, Fact Checkers, developers and investigators, working with governments and organisations around the world in the fields of national security, public safety, election integrity, and public health.
We are verified signatories of the International Fact-Checking Network, with offices in the UK, US and India.
As an MLOps engineer within the DevOps function, you will help build best-in-class Machine Learning(ML) infrastructure to enable Logically’s AI initiatives. You will lead and drive the deployment, life cycle management and monitoring of Machine Learning(ML) and Deep Learning (DL) models in all stages leading to production. You will work directly with Data Scientists and the Data Platform Engineering team to productionize ML/DL models developed for cloud and edge environments. With your experience in MLOps tools and frameworks, you will work to improve Data scientist and Data engineer productivity and delivery speed by enabling them to be more self-sufficient with automated operational processes.
Responsibilities and Performance Objectives:
Define, deploy and manage processes and tools for continuous integration (CI/CD), test-driven development, and release management for ML/DL models (Machine Learning and Deep Learning based) and data pipelines.
Provision and configure secure high performance computing environments (clusters, storage, API gateways etc ) to support hosting of ML/DL models at scale
Code and/or script solutions to automate processes for ML/DL model deployments, retraining, testing and monitoring.
Collaborate with Data Scientists, Data Engineers, ML Engineers, cloud platform and application engineers to create and implement cloud policies and governance for ML/DL model lifecycle.
Ensures adherence to performance standards and compliance to data and model security requirements.
up with emerging best practices in MLOps and drive adoption as necessary.
The wider responsibilities of the DevOps team include
ing and improvise the automated tools to build infrastructure, monitoring and alerting system and visualization
Providing infrastructure resource and support
Providing centralised services and infrastructure for consumption from the rest of Technology, including support for enterprise secrets, logging and observability platforms and Kubernetes as a service
Bachelor’s degree in Computer Science,
Software Engineering or a related field with 5-8 years’ experience.
Experience in AWS, Terraform, Docker, Kubernetes
Strong experience in DevOps combined with AWS technologies such as EC2, ELB, Auto Scaling, Route 53, S3, Cloud Formation, and Cloudwatch
Prior experience in deploying enterprise-scale machine learning solutions. Hands-on experience in one or more MLOps tools such as Kubeflow, MLFlow, Pachyderm, DVC etc.
Strong experience in Deployment (CI/CD) tools: e.g. GitHub Actions or Bitbucket pipelines.
Experience with ETL technologies such as Apache Spark and Databricks
Experience in GCP
Experience with GIT and monitoring and observability solutions
- Unlimited Holidays!
- Company Performance related Bonus
- Private Medical
- A remote-first working location policy
- Ability to work from anywhere in the world for up to 8 weeks of the year
- £250 work from home package to improve your home working environment
- Mental Health Aid
- L&D Perks
- Quarterly Social events
Thank you for your interest in joining Logically.