Job Description
Responsibilities:
• Design the data pipelines and engineering infrastructure to support 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 and inference.
• Identify and evaluate new technologies to improve the 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 audibility, versioning, and data security.
• Facilitate the development and deployment of proof-of-concept machine learning systems.
• Automate the deployment and scaling of machine learning models in production.
• Monitor and maintain the performance and accuracy of machine learning models in production.
• Implement continuous integration and delivery pipelines for machine learning models.
• Collaborate with data scientists, software engineers, and other stakeholders to ensure machine learning models' effective deployment and operation.
• Ensure the security, privacy, and compliance of machine learning models and related data.
• Stay up to date with the latest technology and industry trends in MLOps.
Requirements:
• Bachelor's or Master's degree in computer science, Information Technology, or a related field.
• 3+ years of experience in data engineering or a related field.
• Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or equivalent.
• Experience in MLOps tools like MLFlow or Azure Machine Learning.
• Strong software engineering skills in complex, multi-language systems.
• Fluency in Python.
• 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.
• Ability to translate business needs to technical requirements.
• Strong understanding of software testing, benchmarking, and continuous integration.
It will be good to have:
• The ideal candidate is curious, consultative, well-organized, articulate, and excited about working in a fast-paced environment.
• Ownership mindset: Reliable, takes pride in the quality of their work.
• Hunger: A desire to explore and raise the bar in building search experiences and eagerness to embrace the new and unknown.
• Communicator: Skills to communicate with stakeholders on engineering, product, business, and client teams.
• Design the data pipelines and engineering infrastructure to support 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 and inference.
• Identify and evaluate new technologies to improve the 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 audibility, versioning, and data security.
• Facilitate the development and deployment of proof-of-concept machine learning systems.
• Automate the deployment and scaling of machine learning models in production.
• Monitor and maintain the performance and accuracy of machine learning models in production.
• Implement continuous integration and delivery pipelines for machine learning models.
• Collaborate with data scientists, software engineers, and other stakeholders to ensure machine learning models' effective deployment and operation.
• Ensure the security, privacy, and compliance of machine learning models and related data.
• Stay up to date with the latest technology and industry trends in MLOps.
Requirements:
• Bachelor's or Master's degree in computer science, Information Technology, or a related field.
• 3+ years of experience in data engineering or a related field.
• Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or equivalent.
• Experience in MLOps tools like MLFlow or Azure Machine Learning.
• Strong software engineering skills in complex, multi-language systems.
• Fluency in Python.
• 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.
• Ability to translate business needs to technical requirements.
• Strong understanding of software testing, benchmarking, and continuous integration.
It will be good to have:
• The ideal candidate is curious, consultative, well-organized, articulate, and excited about working in a fast-paced environment.
• Ownership mindset: Reliable, takes pride in the quality of their work.
• Hunger: A desire to explore and raise the bar in building search experiences and eagerness to embrace the new and unknown.
• Communicator: Skills to communicate with stakeholders on engineering, product, business, and client teams.
💡 Quick Summary
Seeking a career-building opportunity? The ML Engineer position is now open for candidates interested in the IT Engineer & Developer Jobs sector. This role in Mumbai offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in IT Engineer & Developer Jobs is a plus.