Python Developer

Hyderabad - India

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

About the Project

We are building a high-performance machine learning engineering platform that powers scalable data-driven solutions for enterprise environments. Your expertise in Python performance optimization and ML tooling will play a key role in shaping intelligent systems for data science and analytics use cases. Experience with MLOps SaaS products or big data environments will be a strong plus.

Role and Responsibilities

Design build and optimize components of the ML engineering pipeline for scalability and performance.
Work closely with data scientists and platform engineers to enable seamless deployment and monitoring of ML models.
Implement robust workflows using modern ML tooling such as Feast Kubeflow and MLflow.
Collaborate with cross-functional teams to design and scale end-to-end ML services across a cloud-native infrastructure.
Leverage frameworks like NumPy Pandas and distributed compute environments to manage large-scale data transformations.
Continuously improve model deployment pipelines for reliability monitoring and automation.

Requirements

5 years of hands-on experience in Python programming with a strong focus on performance tuning and optimization.
Solid knowledge of ML engineering principles and deployment best practices.
Experience with Feast Kubeflow MLflow or similar tools.
Deep understanding of NumPy Pandas and data processing workflows.
Exposure to big data environments and a good grasp of data science model workflows.
Strong analytical and problem-solving skills with attention to detail.
Comfortable working in fast-paced agile environments with frequent cross-functional collaboration.
Excellent communication and collaboration skills.

Nice to Have

Experience deploying ML workloads in public cloud environments (AWS GCP or Azure).
Familiarity with containerization technologies like Docker and orchestration using Kubernetes.
Exposure to CI/CD pipelines serverless frameworks and modern cloud-native stacks.
Understanding of data protection governance or security aspects in ML pipelines.

Experience Required: 5 years

About the ProjectWe are building a high-performance machine learning engineering platform that powers scalable data-driven solutions for enterprise environments. Your expertise in Python performance optimization and ML tooling will play a key role in shaping intelligent systems for data science and ...

About the Project

Role and Responsibilities

Design build and optimize components of the ML engineering pipeline for scalability and performance.
Work closely with data scientists and platform engineers to enable seamless deployment and monitoring of ML models.
Implement robust workflows using modern ML tooling such as Feast Kubeflow and MLflow.
Collaborate with cross-functional teams to design and scale end-to-end ML services across a cloud-native infrastructure.
Leverage frameworks like NumPy Pandas and distributed compute environments to manage large-scale data transformations.
Continuously improve model deployment pipelines for reliability monitoring and automation.

Requirements

5 years of hands-on experience in Python programming with a strong focus on performance tuning and optimization.
Solid knowledge of ML engineering principles and deployment best practices.
Experience with Feast Kubeflow MLflow or similar tools.
Deep understanding of NumPy Pandas and data processing workflows.
Exposure to big data environments and a good grasp of data science model workflows.
Strong analytical and problem-solving skills with attention to detail.
Comfortable working in fast-paced agile environments with frequent cross-functional collaboration.
Excellent communication and collaboration skills.

Nice to Have

Experience deploying ML workloads in public cloud environments (AWS GCP or Azure).
Familiarity with containerization technologies like Docker and orchestration using Kubernetes.
Exposure to CI/CD pipelines serverless frameworks and modern cloud-native stacks.
Understanding of data protection governance or security aspects in ML pipelines.

Experience Required: 5 years