MLOps

3 weeks ago


India Ntigra AI Applications and Services Full time

Company Description

Ntigra is a leader in AI-based RCM Automation solutions for the healthcare industry, dedicated to providing cutting-edge technology innovations to optimize healthcare providers' operations. Our flagship product, Discovery, simplifies workflows, reduces manual processes, and offers tangible benefits like more than 50% denial reduction, over a 60% decrease in manpower costs, and improved cash flow. We take pride in our unique AI technologies, such as Zero-click Automation and Zero-code Integration, and groundbreaking Octopus technology for seamless communication with any EMR or RCM system.

Job Summary: 

We are seeking an experienced MLOps Engineer with 3 + years of hands-on experience in managing and deploying machine learning models at scale. The ideal candidate will have strong expertise in developing and maintaining infrastructure to support the end-to-end machine learning lifecycle, from data preprocessing, model training, and evaluation to model deployment, monitoring, and optimization. 

Role Description

This is a full-time remote role at Ntigra AI Applications and Services. As an MLOps professional, you will be responsible for implementing and managing machine learning pipelines, ensuring smooth deployment, monitoring model performance, and collaborating with cross-functional teams to streamline the machine learning lifecycle.

Key Responsibilities: 

  • End-to-End ML Pipeline Management: Design, develop, and maintain robust, scalable ML pipelines, automating the process of data collection, preprocessing, training, validation, deployment, and monitoring. 
  • Model Deployment: Implement continuous integration (CI) and continuous deployment (CD) systems for ML models using tools such as Kubernetes, Docker, Jenkins, etc. 
  • Infrastructure Development: Collaborate with data scientists and engineers to create and maintain production-ready infrastructure using cloud platforms (Azure , AWS, AnyScale). 
  • Monitoring & Optimization: Monitor deployed models for performance, handle model retraining, and optimize models for improved accuracy and performance. 
  • Collaboration with Teams: Work closely with data scientists, software engineers, and DevOps teams to streamline the ML model lifecycle. 
  • Automation & Scaling: Automate workflows and tasks to efficiently scale ML models in production environments. 
  • Security & Compliance: Ensure that ML pipelines adhere to best practices for security, privacy, and compliance, especially in handling sensitive data. 
  • Documentation: Maintain comprehensive documentation of systems, processes, and best practices for ML pipeline setup, deployment, and maintenance. 

Key Requirements: 

Technical Skills: 

  • Programming Languages: Proficiency in Python, with experience in libraries like TensorFlow, PyTorch, Scikit-learn, and others. 
  • DevOps Tools: Experience with CI/CD tools like Jenkins, CircleCI. 
  • Cloud Platforms: Proficient with cloud services (Azure, AWS, Anyscale) and containerization tools (Docker, Kubernetes). 
  • Automation & Orchestration: Strong experience with ML orchestration tools such as MLflow, Kubeflow, or Airflow. 
  • Monitoring & Logging: Familiarity with logging and monitoring tools like Prometheus, Grafana, ELK stack, or similar. 
  • Version Control: Experience with Git, version control systems, and maintaining repositories for machine learning projects. 

Soft Skills: 

  • Collaboration: Ability to work closely with cross-functional teams. 
  • Problem-solving: Strong analytical skills to troubleshoot and resolve issues in model pipelines and deployment. 
  • Communication: Strong verbal and written communication skills to explain technical details to both technical and non-technical audiences. 

Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. 
  • 3 to 4 years of hands-on experience in machine learning operations (MLOps) and infrastructure management. 
  • Strong knowledge of DevOps principles and cloud architecture for ML applications.
  • Certification in Machine Learning Operations
  • Proficiency in programming languages such as Python, R, or Java
  • Experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn
  • Strong understanding of deployment tools like Kubernetes and Docker
  • Knowledge of cloud platforms such as AWS, GCP, or Azure
  • Ability to work in a collaborative, fast-paced environment
  • Excellent problem-solving and troubleshooting skills

Preferred Qualifications: 

  • Certifications in cloud platforms (AWS, GCP, Azure) or related technologies. 
  • Prior experience with large-scale ML model deployment. 
  • Knowledge of data security and compliance frameworks. 


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