Description

You own the infrastructure and pipelines necessary to move ML models from development into production reliably.

Responsibilities

• Deploy and maintain production-grade machine learning models with real-time inference and scalability.
• Develop end-to-end scalable ML infrastructures on cloud platforms (AWS, Azure, or GCP).
• Implement and optimize CI/CD pipelines specifically for machine learning models, automating testing and deployment.
• Develop AI pipelines for data ingestion, preprocessing, and search/retrieval tasks.
• Collaborate with data science and engineering teams to design robust ML deployment pipelines.

Required Skills

• 5+ years of relevant Machine Learning Engineer experience.
• Proven experience with cloud platforms: AWS, Azure, or GCP.
• Expertise in containerization technologies like Docker and Kubernetes.
• Proficiency in CI/CD tools, specifically Github Actions.
• Strong programming skills in Python, R, and SQL.
• Experience managing the end-to-end ML lifecycle.
• Familiarity with MLOps engineering principles and DevOps life-cycle management.
• Experience implementing automation using Terraform.
• Deep understanding of predictive modeling, LLMs, and NLP.

Key Skills

Education

Bachelor's or Master's degrees