senior-ml-engineer

This Skill deploys ML models into production using Docker and Kubernetes to containerize and orchestrate workloads. It provides scripts for model deployment pipelines, ensuring performance and reliability optimizations for latency and accuracy in production environments.

Integrating LLMs with scalable ML systems involves using frameworks like LangChain, LlamaIndex, and DSPy. This Skill provides guidance on connecting Large Language Models to your existing workflows to add advanced functionalities without disrupting current operations.

Setting up MLOps workflows for large-scale inference requires infrastructure like FastAPI, PostgreSQL, and Redis. This Skill helps you design scalable ML systems and implement MLOps and DataOps tools to ensure smooth operations across your ML infrastructure.

Yes, you can use pgvector and Neo4j within your ML infrastructure for model deployment. This Skill supports designing scalable systems that leverage these databases to manage vector embeddings and graph relationships for advanced inference tasks.

Yes, this Skill supports AWS, GCP, and Azure for production ML operations. It empowers senior ML engineers to implement MLOps best practices and deploy machine learning models across these major cloud platforms to ensure reliable scalability.