Machine learning engineer

1 week ago

Hyderabad, India EverestDX Inc Full time
About Everest DX Everest DX – We are a Digital Platform Services company, headquartered in Stamford. Our Platform/Solution includes Orchestration, Intelligent operations with BOTs’, AI-powered analytics for Enterprise IT. Our vision is to enable Digital Transformation for enterprises to deliver seamless customer experience, business efficiency and actionable insights through an integrated set of futuristic digital technologies.
Digital Transformation Services - Specialized in Design, Build, Develop, Integrate, and Manage cloud solutions and modernize Data centers, build a Cloud-native application, and migrate existing applications into secure, multi-cloud environments to support digital transformation.
Our Digital Platform Services enable organizations to reduce IT resource requirements and improve productivity, in addition to lowering costs and speed digital transformation. For more information, please visit .
Job Title : ML Ops Engineer
Location : Remote
Job Type : Full-Tim
Experience : 6-8 Years of Relevant Work Experience
Job Overview
We are seeking a skilled and experienced ML Ops Enginee r to join our dynamic team. The ideal candidate will have a strong background in ML Ops/Dev Op s, with expertise in configuring CICD pipeline s and working with ADF Pipeline s on Databrick s. You will be responsible for the seamless deployment, integration, and management of machine learning models into production environments. If you have hands-on experience in ML-Flo w and Pytho n, we encourage you to apply and be part of a fast-growing team working on cutting-edge technologies
Key Responsibilities
Configure, manage, and maintai n CI/CD pipelin es for deploying machine learning models and applications in production
Work wit h Azure Data Factory (AD F) pipelines integrated wit h Databric ks to manage data workflows for machine learning projects
Develop, deploy, and monito r machine learning mode ls and ensure seamless integration into production environments
Leverag e ML-Fl ow to manage machine learning lifecycle, including tracking experiments, model training, and deployment
Collaborate with data scientists and engineers to optimize data processing and model deployment workflows
Ensure the scalability, performance, and reliability of production-level ML models and systems
Implement automated testing strategies for ML models and pipelines
Troubleshoot and resolve production issues related to machine learning models, data pipelines, and infrastructure
Participate in the design and development of infrastructure for ML model training and deployment
Required Skills & Qualifications
6-8 years of experie nce in ML Op s, Dev Ops
Strong hands-on experience in configuri ng CI/CD pipeli nes for machine learning workflows
Proven experience working wi th Azure Data Factory (A DF) pipelines a nd Databri cks
In-depth knowledge of ML-F low for model tracking and versioning
Proficiency in Pyt hon for model development, deployment, and automation tasks
Experience with containerization technologies li ke Doc ker and orchestration tools such as Kuberne tes
Solid understanding of cloud platforms (preferab ly Az ure)
Strong problem-solving skills and ability to troubleshoot complex issues related to machine learning pipelines and models
Excellent communication and collaboration skills, with the ability to work in cross-functional teams
Preferred Skills
Familiarity with machine learning frameworks and libraries (e.g., Tensor Flow, Py Torch, Scikit-Learn).
Experience w ith version control sys tems l ike Git
Knowledge of infrastructure-as-code tools l ike Terra form or ARM templ ates
Familiarity w ith monitoring and log ging tools (e.g., Prometheus, Grafana)
Educational Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
Why Join Us
Continuous Learning: The company invests in skills development through training, certifications, and access to cutting-edge projects in digital transformation.
Dynamic, Collaborative Culture: Unlike a large, rigid structure, Everest DX promotes a culture of open communication, creativity, and innovation.
Agile Work Environment: The company operates with a startup mindset, adapting quickly to changes, which can be more stimulating than rigid corporate processes.
Innovative Solutions: Working at a boutique company like Everest DX provides exposure to cutting-edge technologies, creative problem-solving, and innovative product development.
Competitive Salaries: Despite being a boutique company, Everest DX ensures competitive compensation packages that are comparable to industry standards.

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