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MLOps Engineer

1 month ago

Mumbai, Maharashtra, India Talent Socio Full time

Responsibilities :

- Develop, productionize, and deploy scalable, resilient software solutions to integrate and operationalize AI and ML capabilities.

- Adhere to software architecture and software design best practices to write scalable, maintainable, well-designed code.

- Develop automated end-to-end ML delivery pipelines; enabling model training, evaluation, tracking, and serving.

- In collaboration with Data Engineering, design and build feature engineering pipelines for extraction, transformation, and loading of data from a variety of data sources for ML models.

- Advocate for AI security and responsibility best practices. Enable monitoring and observability capabilities for the delivery teams.

- Stay current on new developments in ML frameworks, tooling, data modeling, software architecture, and libraries available for solution development.

- Coach data scientists and data engineers on software development best practices.

- Agile Project Work.

- Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, DevOps engineers, designers, product managers, technical delivery teams, and others to continuously innovate AI and MLOps solutions.

- Act as a positive champion for broader organizations to develop a stronger understanding of software design patterns that deliver scalable, maintainable, well-designed analytics solutions.

- Acts as an expert on complex technical topics that require cross-functional :

- Experience applying continuous delivery best practices, including CI/CD, Version Control, Trunk Based Development, Release Management, and Test-Driven Development using popular tooling (e. g., Azure DevOps, CircleCI).

- Experience deploying and operating scalable, reliable, and secure software solutions on modern clouds (AWS, Azure, GCP) and container platforms (Docker, Kubernetes, etc. ).

- Knowledge of Machine Learning lifecycle (wrangling data, model selection, model training, modeling validation, drift monitoring, and deployment at scale) and experience working with data scientists.

- Familiar with cloud data warehousing solutions such as Snowflake, BigQuery, Fabric, etc.

- Experience with popular MLOps tools (e. g., Kubeflow, MLflow, AzureML, Domino).

Preferred :

- Experience with deep learning platforms and frameworks (e. g. TensorFlow, Keras, etc. ).

- Experience working in Data Architecture, engineering, and ETL teams, managing implementation of projects that utilize big data, advanced analytics, and machine learning technologies.

- Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking to large audiences.

- Demonstrated experience in teaching and/or mentoring profile.

(ref:hirist.tech)