Role Overview :

As a Python Engineer specializing in MLOps, you will be at the forefront of building and scaling production-grade machine learning pipelines. You will work closely with data scientists, infrastructure engineers, and product stakeholders to bridge the gap between experimental models and robust, scalable deployments.

By leveraging Kubeflow for orchestration and Langfuse for LLM observability, you will ensure that our AI systems are not only performant but also transparent and reliable. Your contributions will directly impact the efficiency of our model lifecycle, enabling the business to deploy high-quality AI solutions faster and with greater confidence.

Key Responsibilities :

- Design and implement end-to-end ML pipelines using Kubeflow to automate model training, evaluation, and deployment workflows for our data science teams.

- Integrate Langfuse into existing LLM architectures to monitor performance, track traces, and optimize prompt engineering for improved user outcomes.

- Develop high-performance Python services to support model serving and data processing, ensuring low latency and high availability for end-users.

- Collaborate with cross-functional engineering teams to troubleshoot production issues and refine MLOps best practices, driving continuous improvement in our development lifecycle.

- Maintain comprehensive documentation and monitoring dashboards to provide stakeholders with clear visibility into model health and system performance.

Required Skillset :

- Demonstrated expertise in building production-grade Python applications, with a deep understanding of asynchronous programming and API development.

- Proven ability to orchestrate complex machine learning workflows using Kubeflow, including managing Kubernetes clusters and containerized environments.

- Hands-on experience with Langfuse or similar observability frameworks to manage, debug, and evaluate Large Language Model outputs effectively.

- Strong analytical mindset with the ability to communicate technical complexities to non-technical stakeholders, fostering a collaborative environment across distributed teams.

- Ability to thrive in a remote-first or flexible work environment, maintaining high levels of autonomy and proactive communication with global team members.

- A Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field, supported by 4 - 9 years of professional experience in software or MLOps engineering.