Machine Learning Engineer
2 days ago
Experience: 9+ Years
Location: Mumbai, Mahape
Email ID for CV sharing: tanya@zyoin.com
About the Role
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.
Key Responsibilities:
- Be a hands-on contributor capable of independently designing and developing complete MLOps solutions from scratch.
- 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.
- Manage infrastructure as code (IaC) for reproducibility and scalability.
- 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.
- Hands-on with CI/CD tools – preferably ArgoCD, GitHub Actions, or GitLab CI.
- 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.
- Strong problem-solving, communication, and stakeholder management skills.
- 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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