About us

Liquidity is a high-growth fintech company. Our platform is based on the most advanced artificial intelligence (AI) and machine learning technologies, already making a massive impact on global capital markets. We’re reinventing the future of growth funding for companies around the world by changing how business analysis and investment are done. We’re based in Tel Aviv, NYC, Singapore, London, Abu Dhabi, and Dnipro.

About the Role

We are seeking an experienced MLOps Engineer to join our team. As an MLOps Engineer, you will build and implement ML systems that leverage generative models for various applications. You will work closely with our data scientists and software engineers to develop scalable and robust ML pipelines.

You Will

1. Develop and maintain ML pipelines: Design, build, and optimize workflows that support models from development to deployment. Implement best practices for building and versioning ML models for seamless integration into production systems.

2. Infrastructure management: Collaborate with infrastructure and DevOps teams to provision and maintain the necessary computing resources, including GPUs, clusters, and cloud services, to support generative models.

3. Monitoring and debugging: Set up monitoring systems to track model performance and detect anomalies. Troubleshoot and resolve issues related to model performance, data quality, and infrastructure bottlenecks.

4. Collaboration and documentation: Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand requirements and deliver ML solutions. Document the developed systems, processes, and infrastructure for future reference.

You have

• Minimum 4 years experience as a DevOps Engineer with a focus on MLOps.
• Strong knowledge of CI/CD tools (with Linux, networking)
• Experience with ML lifecycle tools like MLflow, Kubeflow, Seldon, or TFX.
• Proficiency in programming languages like Python and/or R.
• Experience with cloud platforms like AWS, GCP, or Azure.
• Knowledge of data versioning tools like DVC and ML experiment tracking systems.
• Experience with Docker containers, Kubernetes, AWS EKS, helm charts,
• Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
• Strong understanding of ML model deployment strategies and monitoring ML models in production.
• Experience with AWS - an advantage

You ideally (but not necessarily) have:

Certifications in MLOps, AWS, GCP, or Azur

Life at Liquidity:

• We’re global, in six locations and counting!
• We’re direct and friendly – we go by an open-door policy.
• We have set a high bar for our work.
• We work in an adaptable, dynamic, and fast pace environment.
• We're growing - and we want you to grow with us.