ML Ops Engineer

Tensorgo Technologies; Hyderabad; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Tensorgo Technologies in Hyderabad is seeking a seasoned ML Ops Engineer to design and support robust machine learning systems. This role involves transforming offline models into productive environments and driving architectural roadmaps aligned with business needs.

The ideal candidate will possess extensive experience in Python, cloud systems, and ML frameworks, and should have a strong background in integrating scalable solutions using containers and orchestration tools. Candidates can look forward to a dynamic work environment that supports innovation.

Qualifications

• Experience in building end-to-end systems as a Platform Engineer, MLOps Engineer, or Data Engineer.
• Hands-on expertise in Python and ML frameworks.
• Experience with cloud computing and database systems.

Responsibilities

• Design and support enterprise machine learning systems at scale.
• Turn offline models into machine learning production systems.
• Develop scalable tools for machine learning training and inference.

Skills

Python

ML frameworks

Linux administration

Cloud computing

API integrations

Open source tools

Containers/Kubernetes

Data-oriented workflow orchestration

Software testing

Continuous integration

Tools

Prometheus

Grafana

Job description

Profile

We are looking for an experienced and high-energy ML Ops Engineer. The primary function of this role is to design enterprise architecture. Envision and drive solution architecture after hearing the product vision and user stories with ability to envision and drive a proactive architectural roadmap for an existing product keeping in mind the future requirements.

Requirements

• Experience building end-to-end systems as a Platform Engineer, MLOps Engineer, or Data Engineer (or equivalent).
• Hands-on expertise in Python and ML frameworks.
• Expertise with Linux administration.
• Experience working with cloud computing and database systems.
• Experience building custom integrations between cloud-based systems using APIs.
• Experience developing and maintaining ML systems built with open source tools.
• Experience developing with containers and Kubernetes in cloud computing environments.
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.).
• Ability to translate business needs to technical requirements.
• Strong understanding of software testing, benchmarking, and continuous integration.
• Exposure to machine learning methodology and best practices.
• Experience with Prometheus and Grafana integrations for highly scalable environments.

Responsibilities

• Design the data pipelines and engineering infrastructure to support enterprise machine learning systems at scale.
• Take offline models data scientists build and turn them into a real machine learning production system.
• Develop and deploy scalable tools and services to handle machine learning training and inference.
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning systems.
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security.
• Facilitate the development and deployment of proof-of-concept machine learning systems.