Manifest Solutions is currently seeking an LLMOps Engineer - Cloud/Gen AI for an onsite position in Westerville, OH.

• Conceptualize, develop, and execute Machine Learning (ML)/LLM pipelines specifically for Large Language Models, including data acquisition, pre-processing, model training/tuning, deployment, and monitoring.
• Utilize automation tools such as GitOps, CI/CD pipelines, and containerization technologies (Docker, Kubernetes) to streamline ML/LLM tasks across the Large Language Model lifecycle.
• Establish robust monitoring and alerting systems to track Large Language Model performance, data drift, and other key metrics, proactively identifying and resolving issues.
• Perform truth analysis to assess the accuracy and effectiveness of Large Language Model outputs, comparing them to known, accurate data.
• Collaborate closely with infrastructure, DevOps teams, and Generative AI Architects to optimize model performance and resource utilization.
• Oversee and maintain cloud infrastructure (e.g., AWS, Azure) specifically for Large Language Model workloads, ensuring cost-efficiency and scalability.
• Stay current with the latest advancements in ML/LLM Ops, integrating these developments into generative AI platforms and processes.
• Communicate effectively with both technical and non-technical stakeholders, providing updates on the performance and status of Large Language Models.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering or similar.
• At least 5 years of experience as an ML engineer within public cloud platforms.
• Strong programming skills in Python and/or other languages.
• Expertise in cloud platforms (e.g., AWS, Azure) for ML workloads, MLOps, DevOps, or Data Engineering.
• Proven experience in MLOps, LLMOps, or related roles, with hands-on experience deploying and managing machine learning and large language model pipelines.
• Familiarity with generative AI applications and domains such as content creation, data augmentation, style transfer.
• Strong knowledge of Generative AI architectures and methods, including chunking, vectorization, context-based retrieval and search, working with Large Language Models such as Open AI GPT 3.5/4.0, Llama2, Llama3, Mistral, etc.

Preferred Requirements:

• Strong understanding of cybersecurity principles and best practices to ensure the integrity, security, and confidentiality of data.
• Knowledge of AI ethics and understanding how to apply Trustworthy AI to ensure safe, responsible, and ethical use of AI technology.
• Experience with data engineering and data visualization tools and techniques.
• Passion for learning and exploring new generative AI technologies and methods.