We are looking for a Machine Learning Engineer to join our team and contribute to the GenAI initiative. In this position, you will focus on creating, enhancing, and fine-tuning backend systems that drive LLM-powered applications utilizing OpenAI APIs. Your expertise in MLOps, CI/CD, observability, and cloud-native tools will be critical in ensuring the performance, reliability, and scalability of AI-driven solutions.
Responsibilities
Build and enhance backend systems for AI and LLM-powered applications
Integrate LLM applications into cloud platforms and manage their operations
Scale AI systems to meet performance and reliability goals
Create CI/CD pipelines to enable automated deployment processes
Monitor the performance of AI services to ensure system stability
Set up observability and logging to track the performance of LLM APIs
Work with DevOps teams to optimize workflows and improve system reliability
Collaborate with AI and Data Science teams to expand and refine application features
Utilize cloud platforms, particularly Azure, for hosting and scaling AI applications
Design APIs and microservices architecture to enable AI functionalities
Requirements
A minimum of 2 years of experience in Machine Learning Engineering with a focus on backend and software systems
Extensive experience in integrating OpenAI APIs and AI services
Proficiency with MLOps tools such as Orion, ArgoCD, and Opsera for automation of deployments
Experience using monitoring and observability platforms like Grafana, Dynatrace, or ThoughtSpot
Strong knowledge of cloud infrastructure, with a preference for Azure, as well as expertise in Apache Spark and Databricks
Advanced Python programming skills for backend development
Proven experience in developing APIs and designing microservices architectures
Fluency in English, both written and spoken, with a proficiency level of B2+ or higher
Nice to have
Understanding of Data Science concepts and methodologies
Experience working with Large Language Models (LLMs)
Familiarity with Natural Language Processing (NLP) techniques and tools