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: Solid 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
📍 India