Job Description

The ideal candidate should have experience with Seldon core, ML Flow, Istio, Jaeger, Ambassador, Triton, PyTorch, TensorFlow/TFserving (is a plus) and Experience with distributed computing and deep learning technologies such as Apache MXNet, CUDA, cuDNN, TensorRT.
We are using Kubernetes (K8) for their MLOps pipeline orchestration, and this is a powerful and intricate system that involves many moving parts and requires knowledge of related technologies such as Docker, container networking, load balancing, and more.
Hands-on practice is essential, as it requires deploying and managing containerized applications, creating Kubernetes objects, configuring networking and storage, and troubleshooting issues that arise in the system.

Note: We need MLOPS Engineer TensorFlow, TFServing, CudaIstio, Ambassador, Seldon Core, Triton

Under guidance from Senior ML Engineers, design and develop ML models that provides accurate results with controls to solve the business problem identified, using state of art techniques.

Work with cross functional teams - business, technology, and product teams to understand the product vision and build ML solutions that provides brings value to the product
Executes relevant data wrangling activities related to the problem to create viable dataset
Conduct ML experiments to understand the feasibility and build baseline models to solve the business problem
Fine tune the baseline model for optimum performance
Test Models internally per acceptance criteria from the business
Identify areas and techniques to optimize the model based on test results
Document relevant artefacts for communicating with the business
Work with data scientists to deploy the models.
Work with product teams in planning and execution of new product releases.
Set OKRs and success steps for self/ team and provide feedback of goals for team members

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ML Engineer