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MLOps Engineer
4 months ago
Responsibilities :
- Design and implement scalable data pipelines and engineering infrastructure to support machine learning systems at scale.
- Collaborate with data scientists to operationalize and deploy offline models into production-ready systems.
- Develop and deploy robust tools and services to streamline machine learning training, inference, and monitoring processes.
- Evaluate and integrate new technologies to enhance the performance, maintainability, and reliability of machine learning systems.
- Apply best practices of software engineering, including CI/CD, automation, and version control, to machine learning workflows.
- Ensure audibility, versioning, and data security standards are met throughout the model
development lifecycle.
- Lead the development and deployment of proof-of-concept machine learning systems to demonstrate feasibility and efficacy.
- Automate the deployment and scaling of machine learning models in production environments.
- Monitor and maintain the performance, accuracy, and reliability of deployed machine
learning models.
- Establish and manage continuous integration and delivery pipelines for efficient model deployment.
- Collaborate closely with data scientists, software engineers, and other stakeholders to ensure
effective deployment and operation of machine learning models.
- Enforce security, privacy, and compliance measures to safeguard machine learning models and associated data.
- Stay abreast of the latest advancements and trends in MLOps to drive innovation and :
- Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
- 3-7 years of experience in data engineering, software development, or a related field.
- Proven experience in building end-to-end systems as a Platform Engineer, MLOps Engineer, or similar role.
- Hands-on experience with MLOps tools such as MLFlow, Azure Machine Learning, or equivalent.
- Proficiency in Python and strong software engineering skills in developing complex, multi-language systems.
- Experience with cloud computing platforms (e. g., AWS, Azure, GCP) and database systems.
- Demonstrated ability to build custom integrations between cloud-based systems using APIs.
- Familiarity with containerization technologies (e. g., Docker) and Kubernetes in cloud
environments.
- Ability to translate business requirements into technical specifications effectively.
- Solid understanding of software testing, benchmarking, and continuous integration practices.
- Exposure to machine learning methodologies and best practices.
- 2 years of experience working with Cloud Platforms (e. g., Microsoft Azure, AWS, GCP).
- 2 years of experience in Python, Databricks/Hadoop.
- 2 years of experience in MLOps.
Good to have :
- Curiosity, consultative mindset, and eagerness to explore new technologies.
- Ownership mindset with a commitment to delivering high-quality work.
- Strong communication skills to engage with external stakeholders across global clients and the internal team of engineering, product, and business.
Location : Gurgaon
(ref:hirist.tech)
