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

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+

Knowledge of Data Science principles and workflows

Experience with Large Language Models (LLMs)

Understanding of Natural Language Processing (NLP) methodologies and applications