Model Deployment: Package and deploy ML models into production using
containerization (Docker) and orchestration (Kubernetes).
CI/CD Pipelines: Design, build, and maintain continuous integration and continuous
deployment pipelines tailored for machine learning workflows.
Monitoring & Maintenance: Implement observability tools to track model drift, latency,
and performance metrics in real-time.
Infrastructure Management: Provision and manage cloud resources (AWS, GCP, or
Azure) to support model training and inference workloads.
Collaboration: Partner with data scientists to optimize model code for performance and
scalability.
Qualifications & Skills
Experience: 2-3 years of hands-on experience in MLOps, DevOps, or Software
Engineering with a focus on ML systems.
Programming: Robust proficiency in Python and familiarity with shell scripting.
ML Frameworks: Solid understanding of ML libraries (e.g., TensorFlow, PyTorch, Scikit-
Learn) and tracking tools (e.g., MLflow, Weights & Biases).
DevOps Tools: Experience with Docker, Kubernetes, and CI/CD tools (e.g., GitHub
Actions, Jenkins, or GitLab CI).
Cloud Platforms: Familiarity with AWS (SageMaker), GCP (Vertex AI), or Azure
(Machine Learning).

📌 LLM MLOps (India)
🏢 TRIGENT SOFTWARE PRIVATE
📍 India