Machine Learning Ops Engineer, Opus 2
Artificial intelligence
Offers Loyalty Share Scheme
Modernising legal dispute practices
Be an early applicant
Modernising legal dispute practices
201-500 employees
Be an early applicant
Offers Loyalty Share Scheme
201-500 employees
Company mission
To help build future legal practices by making them more agile, connected and learning organisations.
Role
Who you are
- This role is ideal for a hands-on engineer who is passionate about building robust, scalable, and automated systems for machine learning, particularly for cutting-edge LLM-powered applications
- You are a practical and automation-driven engineer. You think in terms of reliability, scalability, and efficiency
- You have hands-on experience building and managing CI/CD pipelines for machine learning
- You're comfortable writing production-quality code, reviewing PR's, and are dedicated to delivering a reliable and observable production environment
- You are passionate about MLOps and have a proven track record of implementing MLOps best practices in a production setting
- You’re curious about the unique operational challenges of LLMs and want to build robust systems to support them
- Experience with model lifecycle management and experiment tracking
- Ability to reason about and implement infrastructure for complex AI systems, including those leveraging vector stores and graph databases
- Proven ability to ensure the performance and reliability of systems over time
- 3+ years of experience in an MLOps, DevOps, or Software Engineering role with a focus on machine learning infrastructure
- Proficiency in Python, with experience in building and maintaining infrastructure and automation, not just analyses
- Experience working in Java or TypeScript environments is beneficial
- Deep experience with at least one major cloud provider (AWS, GCP, Azure) and their ML services (e.g., SageMaker, Vertex AI). Experience with Amazon Bedrock is a significant plus
- Strong familiarity with containerization (Docker) and orchestration (Kubernetes)
- Experience with Infrastructure as Code (e.g., Terraform, CloudFormation)
- Experience in deploying and managing LLM-powered features in production environments
- Bonus: experience with monitoring tools (e.g., Prometheus, Grafana), agent orchestration, or legaltech domain knowledge
What the job involves
- We’re looking for an MLOps Engineer to build and maintain the infrastructure that powers our AI systems
- You will be the bridge between our data science and engineering teams, ensuring that our machine learning models are deployed, monitored, and scaled efficiently and reliably
- You’ll be responsible for the entire lifecycle of our ML models in production, from building automated deployment pipelines to ensuring their performance and stability
- Design, build, and maintain our MLOps infrastructure, establishing best practices for CI/CD for machine learning, including model testing, versioning, and deployment
- Develop and manage scalable and automated pipelines for training, evaluating, and deploying machine learning models, with a specific focus on LLM-based systems
- Implement robust monitoring and logging for models in production to track performance, drift, and data quality, ensuring system reliability and uptime
- Collaborate with Data Scientists to containerize and productionize models and algorithms, including those involving RAG and Graph RAG approaches
- Manage and optimize our cloud infrastructure for ML workloads on platforms like Amazon Bedrock or similar, focusing on performance, cost-effectiveness, and scalability
- Automate the provisioning of ML infrastructure using Infrastructure as Code (IaC) principles and tools
- Work closely with product and engineering teams to integrate ML models into our production environment and ensure seamless operation within the broader product architecture
- Own the operational aspects of the AI lifecycle, from model deployment and A/B testing to incident response and continuous improvement of production systems
- Contribute to our AI strategy and roadmap by providing expertise on the operational feasibility and scalability of proposed AI features
- Collaborate closely with Principal Data Scientists and Principal Engineers to ensure that the MLOps framework supports the full scope of AI workflows and model interaction layers
Salary benchmarks
Our take
Documentation, clients and teams are all multifarious in legal proceedings, and therefore provide unique logistical problems. Opus 2 aims to meet the needs of the modern courtroom by providing digital solutions in an age of remote hearings.
The company provides e-bundles that seek to solve these problems. Whilst courtrooms may use existing video software to conduct hearings, Opus 2 integrates this software with other elements (such as data storage and documentation) in order to streamline the process and ultimately save time for everyone. The company offers virtual workspaces on a secure cloud platform, streamlining legal work for both lawyers and clients.
Opus 2 continually invests in developing its software in the UK and internationally and facilitates this by hiring new talent. A recent partnership with SeeUnity is set to help achieve this.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
13% employee growth in 12 months
Company
Company benefits
- Accessible and modern office spaces fully kitted out with Table Tennis and regular company social events
- Contributory pension plan
- 26 days annual holidays, flexible working, and length of service entitlement
- Enhanced Maternity and Paternity
- Employee Assistance Programme
- Season ticket loan
- Cycle to Work Scheme
- Calm and Mindfulness sessions
- Perkbox scheme
- A day of leave to volunteer for charity and an active CSR team raising money towards a company charity
Company HQ
Holborn, London, UK
Leadership
Graham Smith-Bernal
(CEO)Founded LiveNote, the world's most widely used litigation support tool, before starting Opus 2 in 2008.
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