Job Title: ML Ops Engineer

Experience: 2-4 years

About InOrg:

InOrg Empowering Global Growth at Scale.

Headquartered in Charlotte, US, InOrg helps organizations achieve global competitiveness through the seamless integration of talent, technology, and strategic capabilities. Inorg's mission is to provide holistic support to organizations establishing their Global Capability Centers (GCC) in India. It goes beyond merely offering infrastructure and HR assistance; we aim to be strategic partners, aiding in the seamless establishment and operational success of GCCs

Job Description:

As an ML Ops Engineer, you will be responsible for developing and maintaining the infrastructure and tools required to deploy and operate machine learning models. You will work closely with data scientists, software engineers, and IT professionals to ensure our machine learning solutions are scalable, reliable, and efficient.

Key Responsibilities:

  • Develop, implement, and maintain CI/CD pipelines for machine learning models.
  • Monitor and manage the performance of deployed models, ensuring high availability and reliability.
  • Automate data preprocessing, model training, and model deployment processes.
  • Collaborate with data scientists to optimize model performance and scalability.
  • Implement robust version control for data, models, and code.
  • Ensure compliance with data security and privacy policies.
  • Perform regular maintenance and updates of ML infrastructure.
  • Troubleshoot and resolve issues related to ML model deployment and operations.

Required Qualifications:

  • Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
  • 2-4 years of experience in ML Ops, DevOps, or related roles.
  • Proficiency in programming languages such as Python, Java, or C++.
  • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
  • Strong understanding of CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI).
  • Knowledge of data engineering concepts and tools (e.g., Apache Kafka, Apache Spark).
  • Excellent problem-solving skills and attention to detail.
  • Strong communication and collaboration skills.

Preferred Qualifications:

  • Experience with monitoring tools and practices (e.g., Prometheus, Grafana).
  • Understanding of model interpretability and monitoring metrics.
  • Familiarity with data version control systems (e.g., DVC).
  • Knowledge of microservices architecture.


  • Competitive salary and benefits package.
  • Opportunity to work with cutting-edge technologies.
  • Collaborative and innovative work environment.
  • Professional development and growth opportunities.


  • Expertise in MLOps
  • Proficiency in Python is mandatory
  • Strong leadership, mentorship, and strategic thinking capabilities.
  • Deep domain knowledge in one or more industries, with the ability to apply MLOps principles effectively.
  • Excellent communication skills, capable of driving innovation and change.