Machine Learning Operations

Dear Candidates,

We are looking for someone with at least 3 Years of experience in Machine Learning.

Please find the Job Details as below;

JOB PROFILE: Machine Learning Operations (MLOps)

LOCATION: Gurugram

EXPERIENCE: 3 to 10 Years

ROLES & RESPONSIBILITIES:

A) 3 to 5 Years

Experience in MLOps LLMOps Managed Services production support projects

Pipeline Development: Design, build, and maintain CI/CD (Continuous Integration/Continuous Deployment) pipelines for automated model training, testing, and deployment.

Automation: Automate workflows for model versioning, experimentation, and model retraining to ensure continuous improvement.

Deployment and Integration: Deploy and integrate ML models into production environments, ensuring scalability and reliability.

Monitoring and Management: Implement and manage monitoring tools to track model performance, system health, and resource utilization.

Collaboration: Work closely with data scientists, data engineers, and software engineers to define requirements and integrate models into broader platforms.

Troubleshooting: Identify and resolve issues in development, testing, and production environments related to models and the underlying infrastructure.

Documentation: Maintain accurate and comprehensive documentation of MLOps processes, tools, and systems.

Technical Requirements:

Build, train, and optimize machine learning models using frameworks such as TensorFlow, PyTorch, and Scikit-learn.

Leverage Open-Source Large Language Models (OSS LLMs), such as Hugging Face Transformers, Llama, GPT-J, and Falcon for innovative applications.

Evaluate and compare the performance of various ML and GenAI models using metrics such as precision, recall, AUC-ROC, and F1-score.

Deploy machine learning and GenAI models into production using MLOps tools like MLflow, Kubeflow, AWS SageMaker, LiteLLM, Comet Opik.

Cloud Platforms: Experience with cloud providers such as AWS, Azure, and GCP is often required for building scalable cloud-native solutions.

MLOps Platforms: Proficiency with MLOps platforms like MLflow, Kubeflow, DataRobot, or Dataiku. LiteLLM, Comte Opik.

CI/CD Tools: Familiarity with CI/CD orchestration tools such as GitLab CI, GitHub Actions, and Airflow.

Containerization: Expertise in containerization technologies like Docker.

Programming Languages: Strong programming skills, often in Python, for scripting and automation.

Monitoring Tools: Experience with Ai Observability / monitoring tools like Prometheus and Grafana

B) For 6 to 10 Years

Experience in MLOps LLMOps Managed Services production support projects

Pipeline Development: Design, build, and maintain CI/CD (Continuous Integration/Continuous Deployment) pipelines for automated model training, testing, and deployment.

Automation: Automate workflows for model versioning, experimentation, and model retraining to ensure continuous improvement.

Deployment and Integration: Deploy and integrate ML models into production environments, ensuring scalability and reliability.

Monitoring and Management: Implement and manage monitoring tools to track model performance, system health, and resource utilization.

Collaboration: Work closely with data scientists, data engineers, and software engineers to define requirements and integrate models into broader platforms.

Troubleshooting: Identify and resolve issues in development, testing, and production environments related to models and the underlying infrastructure.

Documentation: Maintain accurate and comprehensive documentation of MLOps processes, tools, and systems.

Technical Requirements:

Build, train, and optimize machine learning models using frameworks such as TensorFlow, PyTorch, and Scikit-learn.

Leverage Open-Source Large Language Models (OSS LLMs), such as Hugging Face Transformers, Llama, GPT-J, and Falcon for innovative applications.

Evaluate and compare the performance of various ML and GenAI models using metrics such as precision, recall, AUC-ROC, and F1-score.

Deploy machine learning and GenAI models into production using MLOps tools like MLflow, Kubeflow, AWS SageMaker, LiteLLM, Comet Opik.

Cloud Platforms: Experience with cloud providers such as AWS, Azure, and GCP is often required for building scalable cloud-native solutions.

MLOps Platforms: Proficiency with MLOps platforms like MLflow, Kubeflow, DataRobot, or Dataiku. LiteLLM, Comte Opik.

CI/CD Tools: Familiarity with CI/CD orchestration tools such as GitLab CI, GitHub Actions, and Airflow.

Containerization: Expertise in containerization technologies like Docker.

Programming Languages: Strong programming skills, often in Python, for scripting and automation.

Monitoring Tools: Experience with Ai Observability / monitoring tools like Prometheus and Grafana

If interested, Kindly apply on the below given link,

Machine Learning Operations - Gurgaon Fill out form