Machine Learning Engineer

2 days ago

Mumbai, Maharashtra, India Zyoin Group Full time
Position: Lead MLOps Engineer Experience: 9+ Years

We are looking for a Lead MLOps Engineer who will lead and support the end-to-end deployment, monitoring, and optimization of machine learning and data-driven applications across cloud platforms. You will collaborate with data scientists, engineers, and business stakeholders to ensure scalable, secure, and highly available ML systems.

Lead end-to-end ML pipeline development, deployment, and monitoring across GCP and Azure .

Build and maintain CI/CD pipelines using tools like ArgoCD, Git, and Docker.

Automate and optimize ML model training, validation, deployment, and scaling using Kubernetes, Kubeflow, or similar orchestration platforms .

Develop data processing workflows using Python and PySpark on distributed systems.

Implement observability using tools like Grafana, NewRelic , and cloud-native monitoring solutions.

Collaborate with Data Scientists to transition research into production-grade solutions.

Guide and mentor junior engineers, enforce coding standards, and conduct code reviews.

Demonstrate business understanding to align ML pipelines with product goals.

Exposure to AI and RAG-related development , various GPU and AI Platforms required.

Required Skills & Experience:

~9+ years of hands-on experience in MLOps roles.

~ Strong proficiency in Python and PySpark with clean and scalable code practices.

~ Expertise in GCP and Azure cloud platforms – including compute, storage, and networking components.

~ Proven experience in deploying and managing containerized applications using Docker and Kubernetes .

~ Experience in monitoring, logging, and alerting using tools such as Grafana, NewRelic, Prometheus , or similar.

~ Understanding of ML model lifecycle, versioning, and performance monitoring.

~ Experience with MLFlow for model versioning.

~ Ability to create REST APIs using FastAPI, Flask, or Django .

~ Experience mentoring teams and driving end-to-end project execution.

~ Exposure to Vertex AI Pipeline in GCP or similar in other clouds is a plus.

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