Role overview

Qualifications

10+ years of professional experience in building applications using cloud services
Deep knowledge of cloud platforms like AWS, Google Cloud Platform, or Azure
Experience with CI/CD pipelines, infrastructure as code, and containerization technologies
Understanding of ML workflows, model training, and deployment processes

Responsibilities

Evaluate and select appropriate cloud services for each stage of the ML lifecycle
Design and implement the overall architecture of the MLOps platform
Set up automated pipelines for data preparation, model training, and deployment
Ensure the platform is scalable, secure, and compliant with relevant regulations

Key facts

Remote from: India
Full time
Expert & Leadership (>10 years)
Platform Engineer
English

Hard skills

Machine Learning CI/CD Python (Programming Language) Amazon Web Services Google Cloud Platform (GCP) Microsoft Azure Kubernetes Data Engineering Systems Design Personal Security Continuous Monitoring

Other skills

Collaboration

About the company

Veracity Software Inc

The definition of Veracity is accuracy and habitual truthfulness. This truly defines our brand in simplistic terms. At Veracity, we believe in doing everything in the ‘Right Way’, which means you have to challenge your own innovations and solutions all the time. Our strength lies in our employees, and with the right blend of business and technical expertise, we set the bar high for Veracity. From inception, Veracity focused on building cloud based SaaS solutions while using the latest and most advanced technology stack. We have delivered cloud based customized solutions and services to various clients across both North America and India. We have a global headquarter in Woodbridge, NJ while our offshore office is in Pune, India. We believe in bringing Global talent together, which helps us in delivering cost effective, yet highly scalable and efficient solutions. With the vision of trying to be one of the trusted IT partners as well as one of the most respected solution providers with an onsite/offshore model, Veracity is equipped with the right set of highly skilled individuals who have expertise in delivering the most effective business services and solutions for our clients. Veracity’s mission is to become the Trusted IT Partner for various businesses across globe.

Company details

Company size51 - 200

Links

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Job description

ML Platform Engineer
Location: India
Work mode: Remote / FTE

Turing is looking for people to join us in building ML platforms for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization heading a team of other Turing engineers across different skill sets.

Required skills

10+ years of professional experience in building applications using cloud services. Prior experience in building Machine Learning platforms using cloud services.
Cloud expertise: Deep knowledge of cloud platforms like AWS, Google Cloud Platform, or Azure, including their machine learning and data services (Azure preferred).
DevOps skills: Experience with CI/CD pipelines, infrastructure as code, and containerization technologies like Docker and Kubernetes.
Machine learning knowledge: Understanding of ML workflows, model training, and deployment processes.
Data engineering: Familiarity with data pipelines, ETL processes, and data storage solutions.
Software engineering: Strong programming skills, particularly in languages commonly used in ML like Python.
System design: Ability to architect scalable, reliable systems that integrate various services.
Automation: Expertise in automating workflows and processes across the ML lifecycle.
Security and compliance: Knowledge of best practices for securing ML pipelines and ensuring regulatory compliance.
Monitoring and logging: Experience setting up monitoring and logging for ML systems.
Collaboration: Ability to work with data scientists, software engineers, and other stakeholders.

Roles & responsibilities

Evaluate and select appropriate cloud services for each stage of the ML lifecycle
Design and implement the overall architecture of the MLOps platform
Set up automated pipelines for data preparation, model training, and deployment
Implement version control for code, data, and models
Ensure the platform is scalable, secure, and compliant with relevant regulations
Provide tools and interfaces for data scientists to easily leverage the platform
Continuously optimize the platform for performance and cost-efficiency
This role is crucial in bridging the gap between data science and operations, enabling organizations to efficiently develop, deploy, and maintain machine learning models at scale.