Part-Time Remote MLOps Engineer
Budget: ₹750 – ₹1,250 INR
I'm looking for a part-time, remote MLOps engineer who can help with a range of tasks related to model deployment, data pipeline management, and monitoring and maintenance.
Role Overview
We are seeking a skilled Part-Time MLOps Engineer with 3+ years of experience to drive the deployment, monitoring, and automation of machine learning models in production environments. As a part-time contributor, you will collaborate remotely with our data science and engineering teams to ensure efficient workflows and robust model performance.
Key Responsibilities
Design, build, and maintain MLOps pipelines for model deployment, monitoring, and retraining.
Automate workflows for continuous integration, delivery, and deployment (CI/CD) of ML models.
Monitor model performance and address data drift, scalability, and operational issues.
Manage containerized deployments using tools like Docker and Kubernetes.
Collaborate with cross-functional teams to integrate ML models into production systems.
Optimize infrastructure for ML model serving on cloud platforms (AWS, GCP, or Azure).
Document processes, workflows, and best practices for MLOps.
Qualifications
Must-Have Skills:
Proven experience in MLOps tools and frameworks (e.g., MLflow, Kubeflow, TensorFlow Serving).
Strong programming skills in Python and ML libraries (e.g., PyTorch, TensorFlow, scikit-learn).
Expertise in containerization and orchestration (Docker, Kubernetes).
Proficiency in cloud platforms (AWS, GCP, or Azure) for ML infrastructure.
Experience with version control systems (Git) and CI/CD pipelines.
Solid understanding of machine learning workflows and challenges in production.
Preferred Skills:
Knowledge of monitoring and logging tools (Prometheus, Grafana, ELK stack).
Experience in data engineering concepts (e.g., ETL pipelines, data preprocessing).
Familiarity with DevOps practices and tools (e.g., Jenkins, CircleCI).
Role Overview
We are seeking a skilled Part-Time MLOps Engineer with 3+ years of experience to drive the deployment, monitoring, and automation of machine learning models in production environments. As a part-time contributor, you will collaborate remotely with our data science and engineering teams to ensure efficient workflows and robust model performance.
Key Responsibilities
Design, build, and maintain MLOps pipelines for model deployment, monitoring, and retraining.
Automate workflows for continuous integration, delivery, and deployment (CI/CD) of ML models.
Monitor model performance and address data drift, scalability, and operational issues.
Manage containerized deployments using tools like Docker and Kubernetes.
Collaborate with cross-functional teams to integrate ML models into production systems.
Optimize infrastructure for ML model serving on cloud platforms (AWS, GCP, or Azure).
Document processes, workflows, and best practices for MLOps.
Qualifications
Must-Have Skills:
Proven experience in MLOps tools and frameworks (e.g., MLflow, Kubeflow, TensorFlow Serving).
Strong programming skills in Python and ML libraries (e.g., PyTorch, TensorFlow, scikit-learn).
Expertise in containerization and orchestration (Docker, Kubernetes).
Proficiency in cloud platforms (AWS, GCP, or Azure) for ML infrastructure.
Experience with version control systems (Git) and CI/CD pipelines.
Solid understanding of machine learning workflows and challenges in production.
Preferred Skills:
Knowledge of monitoring and logging tools (Prometheus, Grafana, ELK stack).
Experience in data engineering concepts (e.g., ETL pipelines, data preprocessing).
Familiarity with DevOps practices and tools (e.g., Jenkins, CircleCI).