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MLOps Engineer - CI / CD Pipeline

MLOps Engineer - CI / CD Pipeline

AccordinnovationsDelhi, IN
30+ days ago
Job description

Location : Johor, Malaysia

Duration : 12 Month extendable contract

Experience : 5-8 years

Visa will be sponsored( should be able to relocate)

Key Responsibilities :

ML System Development & Deployment :

  • Develop, deploy, and maintain end-to-end machine learning systems using Python.

Containerization & Orchestration :

  • Package and manage ML applications using containerization tools like Docker and Podman.
  • Orchestrate these containers for large-scale deployment and management with platforms such as Kubernetes or Docker Swarm.

    • CI / CD Pipeline Management :

  • Design and implement continuous integration and continuous deployment (CI / CD) pipelines for ML models using tools like Git, Jenkins, and GitHub Actions.

    • Monitoring & Logging :

  • Establish comprehensive monitoring and logging strategies for ML models in production to ensure performance, stability, and data integrity using tools like ELK Stack, Prometheus, and Telegraf.

    • Data Streaming & Integration :

  • Work with data streaming platforms such as Apache Kafka, Flink, and RabbitMQ to build real-time data pipelines for model training and inference.

    • Infrastructure & Configuration Management :

  • Utilize configuration and infrastructure tools like Ansible, Puppet, or SaltStack to automate the setup and management of the ML infrastructure.

    • Database Management :

  • Interact with and manage various databases, including relational (e.g , PostgreSQL, MySQL) and NoSQL (e.g , MongoDB, Redis), to support ML workflows.

    • Model Serving & API Development :

  • Deploy and serve trained AI models using specialized frameworks like TensorFlow Serving, ONNX Runtime, or Nvidia Triton, and develop robust API services with FastAPI and Streamlit.

    • Education :

  • A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Applied Mathematics, Physics, or a related technical field.
  • Equivalent hands-on experience in AI / ML engineering, DevOps, or systems architecture may also be considered.

    • Required Experience :

  • Experience in developing and deploying machine learning systems using Python, containerization tools like Docker and Podman, and Linux-based operating systems such as Ubuntu or RHEL.
  • Experience with orchestration platforms like Kubernetes or Docker Swarm, and CI / CD tools such as Git, Jenkins, and GitHub Actions.
  • Proficiency in monitoring and logging tools such as ELK Stack, Fluentd, Prometheus, Telegraf, and various data streaming platforms like Apache Kafka, Flink, Storm, and RabbitMQ.
  • Practical knowledge of relational and NoSQL databases such as PostgreSQL, MariaDB, MySQL, MongoDB, Redis, and InfluxDB.
  • Hands-on experience with AI / ML frameworks like TensorFlow, PyTorch, Transformers, Scikit-learn, Ollama, LangChain, and CrewAI.
  • Familiarity with configuration and infrastructure tools including Ansible, Puppet, SaltStack, as well as visualization libraries such as Grafana, Kibana, Matplotlib, and Plotly.
  • Working knowledge of AI model deployment frameworks such as TensorFlow Serving, ONNX Runtime, TorchServe, Nvidia Triton, and API services using FastAPI and Streamlit.

    • Certifications (Preferred) :

  • AWS Certified Machine Learning - Specialty
  • Certified Kubernetes Administrator (CKA)
  • TensorFlow Developer Certificate
  • Microsoft Azure AI Engineer Associate
  • Certified MLOps Engineer from recognized training platforms (e.g, Coursera, DataCamp, Udacity)

    • (ref : hirist.tech)

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