We are seeking a Machine Learning Engineer to join our team and support the GenAI initiative. In this role, you will focus on designing, improving, and optimizing backend infrastructure to power LLM-based applications using OpenAI APIs. Your skills in MLOps, CI/CD, observability, and cloud-native technologies will be essential to ensure the reliability, scalability, and efficiency of AI-driven systems.
Responsibilities
Develop and improve backend infrastructure for AI and LLM-based solutions
Integrate and oversee LLM applications within cloud environments
Scale AI systems to meet performance and reliability requirements
Implement automated deployment processes through CI/CD pipelines
Track and maintain the performance of AI services to ensure consistency
Establish logging and observability frameworks for monitoring LLM API performance
Collaborate with DevOps teams to streamline workflows and enhance system dependability
Work closely with AI and Data Science teams to develop and enhance application features
Leverage cloud platforms, especially Azure, to deploy and scale AI-powered applications
Design and build APIs and microservices to support AI-driven functionalities
Requirements
At least 2 years of experience in Machine Learning Engineering with a focus on backend and software development
Strong expertise in integrating and working with OpenAI APIs and other AI services
Hands-on experience with MLOps tools such as Orion, ArgoCD, and Opsera for deployment automation
Proficiency with monitoring and observability tools, including Grafana, Dynatrace, and ThoughtSpot
Comprehensive knowledge of cloud platforms, particularly Azure, as well as Apache Spark and Databricks
Advanced Python programming skills for backend development and implementation
Proven experience in designing and building APIs and microservices architecture
Fluency in English, both verbal and written, with a minimum proficiency level of B2+
Nice to have
Knowledge of Data Science principles and workflows
Experience with Large Language Models (LLMs)
Understanding of Natural Language Processing (NLP) methodologies and applications