Associate - Business Analyst-Data Engineering-Cloud Data Engineering

On-siteMidMLOpsposted 11mo ago

✦Role summaryAI-generated

This role bridges cloud data engineering and MLOps, requiring hands-on expertise in orchestrating ML pipelines, optimizing containerized deployments, and collaborating with data scientists to resolve model execution challenges. Candidates should demonstrate proficiency in cloud platforms, MLOps tools like Feast, and problem-solving for scalable ML deployments.

Skills required

Python Kubernetes Docker Azure GCP Feast Gurobi MLOps Model registration Container optimization Machine learning Model serving Kubeflow XGBoost PyTorch TensorFlow SQL

About this role

Strong hands-on programming skills in Python with multiple design patterns.
Strong hands-on expertise in Kubernetes and Docker, having ability to build and optimize container images.
Experience with Azure/GCP cloud platform, machine learning services and best practices.
Strong problem-solving abilities with a capacity to quickly dive deep into issues and propose effective solutions.

Ability to manage multiple concurrent tasks and prioritize

Responsibilities

Experience in collaborating with data scientist to solve ML model execution related challenges, and able to suggest different solution architecture for overcoming the challenges.
Experience with machine learning tools among phases in MLOps life cycle such as feature store (FEAST), model registry, problem optimizer (GUROBI) or real-time model serving.
Preferred Certifications:
- CKA – Certified Kubernetes Administrator
- CKAD – Certified Kubernetes Application Developer
Good Understanding of machine learning platforms such as Kubeflow and VertexAI, and frameworks such as XGBoost, PyTorch and TensorFlow.
hands-on expertise in SQL.

Qualifications

Experience in collaborating with data scientist to solve ML model execution related challenges, and able to suggest different solution architecture for overcoming the challenges.
Experience with machine learning tools among phases in MLOps life cycle such as feature store (FEAST), model registry, problem optimizer (GUROBI) or real-time model serving.
Preferred Certifications:
- CKA – Certified Kubernetes Administrator
- CKAD – Certified Kubernetes Application Developer
Good Understanding of machine learning platforms such as Kubeflow and VertexAI, and frameworks such as XGBoost, PyTorch and TensorFlow.
hands-on expertise in SQL.