REQUIREMENTS:

• 3+ years of experience in software engineering, DevOps, or a related field, with proven expertise in consulting on deploying and managing machine learning models in production,
• proficiency in working with cloud platforms such as AWS, GCP, and Azure, as well as infrastructure-as-code tools like Terraform and Bicep,
• strong skills in deploying AI solutions on AWS and building cloud-based AI applications on Azure,
• experience in fine-tuning pre-trained language models for specific tasks, evaluating their performance, and deploying large language models (LLMs) in production environments,
• proficiency in using APIs of popular LLMs, such as GPT-3 and BERT,
• demonstrated ability to implement DevOps, MLOps, and LLMOps practices for AI projects, ensuring streamlined and efficient workflows throughout the ML lifecycle.

• provide expert guidance on deploying and monitoring ML models using cloud platforms like Azure and AWS,
• assist clients in automating ML environment provisioning with tools such as Docker, Kubernetes, and Terraform,
• develop and manage CI/CD pipelines for efficient and reliable model deployment,
• ensure seamless integration of ML models with client systems and recommend scalable infrastructure for real-time and batch predictions,
• collaborate with teams to deploy models into production effectively,
• create monitoring tools and alerting mechanisms to track model performance, detect drift, and address data quality issues,
• advise on regular maintenance and updates for ML models and infrastructure,
• stay up-to-date with advancements in MLOps, cloud technologies, and machine learning frameworks. Drive automation and efficiency in the ML lifecycle and provide strategic insights to optimize clients’ MLOps processes.