Junior MLOps Engineer
Role Overview
As a Junior MLOps Engineer at Zzazz, you will work closely with data science teams to design, build, and deploy scalable ML pipelines and microservices. Your day-to-day responsibilities include developing Python-based microservices, managing message queue systems, and deploying machine learning models into production environments. This entry-level position will have a significant impact on optimizing ML workflows and ensuring the reliability of deployed models.
Perks & Benefits
This role offers a fully remote work setup with a standard schedule of 9 AM to 6 PM IST, Monday through Friday. While the position is remote, it provides opportunities for career growth within a pioneering company in the AI and economics space. The team culture emphasizes innovation and collaboration, allowing you to contribute to meaningful projects that reshape industries worldwide.
Full Job Description
Role : Junior MLOps Engineer
Location : Shezan Lavelle, Bengaluru
Timings : 9 AM - 6 PM IST
Workdays : 5 days from office (Monday- Friday)
About ZZAZZ:
ZZAZZ is pioneering the next global information economy by transforming how digital content is valued and experienced. Our advanced Economic AI quantifies the real-time value and price of
content, turning it into dynamic assets that reshape industries worldwide. At ZZAZZ you'll join a
team of innovators pushing the boundaries of AI and economics to create meaningful impact on a global scale.
About the Role
We are looking for a motivated MLOps Engineer with solid backend engineering and deployment
experience. The ideal candidate will help design, build, and deploy scalable ML pipelines and
microservices while ensuring seamless model integration into production environments.
Key Responsibilities
• Develop, maintain, and optimize Python-based microservices using FastAPI / Flask /Django.
• Design and manage message queue systems with Kafka or RabbitMQ for large-scale data pipelines.
• Work with databases like MongoDB, Elasticsearch, Cosmos DB, OpenSearch, or Qdrant for
storage and retrieval.
• Deploy and monitor machine learning models in production environments.
• Collaborate with data science teams to operationalize ML workflows.
• Implement CI/CD pipelines for ML and data systems.
• Ensure reliability, scalability, and performance of deployed models and services.
Mandatory Skills
• Programming: Python
• Message Queues: Kafka / RabbitMQ
• Databases: MongoDB / Elasticsearch / Cosmos DB / OpenSearch / Qdrant
• Web Frameworks: FastAPI / Flask / Django
• Model Deployment: Experience deploying ML models into production
• ML Knowledge: Basic understanding of ML models and lifecycle
Good to Have
• Containerization & Orchestration: Docker / Kubernetes
• GPU Workloads: Exposure to GPU-based computation or deployment environments
• Monitoring Tools: Experience with model and infrastructure monitoring tools
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