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

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

Understanding of Data Science concepts and methodologies

Experience working with Large Language Models (LLMs)

Familiarity with Natural Language Processing (NLP) techniques and tools