Product Engineer

(*Note: This is a requirement for one of Uplers' client - IF)What do you need for this opportunity?

Product Engineer

Location: Narsingi, Hyderabad

5 days of work from the Office

Client is a Payment gateway processing company

Interview Process: Screening round with InfraCloud, followed by a second round with our Director of Engineering. We share the profile with the client, and they take one/two interviews

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