Machine Learning Engineer

1 month ago

Delhi, India EverestDX Inc Full time
About EverestDX

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/DevOp 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., TensorFlow, PyTorch, 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

1. Continuous Learning: The company invests in skills development through training, certifications, and access to cutting-edge projects in digital transformation.2. Dynamic, Collaborative Culture: Unlike a large, rigid structure, Everest DX promotes a culture of open communication, creativity, and innovation.3. Agile Work Environment: The company operates with a startup mindset, adapting quickly to changes, which can be more stimulating than rigid corporate processes.4. Innovative Solutions: Working at a boutique company like Everest DX provides exposure to cutting-edge technologies, creative problem-solving, and innovative product development.5. Competitive Salaries: Despite being a boutique company, Everest DX ensures competitive compensation packages that are comparable to industry standards.

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