Machine Learning Engineer at Ola

Short Description:

We are looking for a skilled Machine Learning Engineer to join our team, focusing on deploying and optimizing ML/Deep Learning models in a production environment. Responsibilities include implementing model workflows, overseeing version tracking and governance, and scaling DL Models. The role involves collaborating with Data Scientists and Engineers, employing best practices for efficient model operations, and developing scalable MLOps frameworks. The ideal candidate possesses experience with container technologies, cloud providers, and proficiency in distributed computing, with a preferred tech stack including Python and MLOps tools like MLFlow, Kubernetes, and Docker. Join us in advancing the field of Machine Learning and contributing to cutting-edge solutions.

---

 

Position: Machine Learning Engineer

Company: Ola

Location: Bengaluru, Karnataka, India

Jon Type: Full time / On-site

In our dynamic and innovative workplace, we are actively seeking a highly skilled Machine Learning Engineer to join our team. In this pivotal role, you will be instrumental in the deployment, operationalization, and optimization of ML/Deep Learning models within a production environment. The multifaceted nature of this position entails a broad array of responsibilities that are crucial for the success of our machine learning initiatives.

Responsibilities:

1. Model Hyperparameter Selection: As a Machine Learning Engineer, you will be responsible for implementing model hyperparameter selection strategies. This involves fine-tuning the parameters of machine learning models to enhance their performance and accuracy.

2. Model Evaluation and Explainability Analysis: Conducting thorough model evaluation and explainability analysis is a key facet of this role. You will be tasked with assessing the effectiveness and interpretability of the models deployed, ensuring transparency in the decision-making process.

3. Model Workflows Management: Overseeing the entire lifecycle of machine learning models, from onboarding and operations to decommissioning, is a critical aspect of your responsibilities. This includes designing efficient workflows that align with organizational objectives.

4. Model Version Tracking and Governance: Implementing robust model version tracking and governance mechanisms is vital for maintaining the integrity and traceability of models throughout their lifecycle. You will ensure adherence to established governance policies.

5. Data Archival and Version Management: Managing data archival and version management processes is part of your role. This includes organizing and maintaining different versions of datasets and models to facilitate efficient collaboration and future reference.

6. Model and Drift Monitoring: Regular monitoring of model performance and detecting drift is essential for ensuring the ongoing effectiveness of deployed models. Your role includes implementing monitoring systems to identify deviations and initiate corrective actions.

7. DL Models Deployment and Scaling: You will be responsible for deploying and scaling Deep Learning (DL) models, ensuring they seamlessly integrate into the production environment and can handle varying workloads efficiently.

8. Benchmarking, Metrics, and Monitoring Strategies: Developing benchmarks, metrics, and monitoring strategies is integral to continually measure and enhance the performance of machine learning services. This involves creating frameworks to track and analyze key performance indicators.

9. Best Practices and POC Execution: Offering best practices for automated and efficient model operations at scale is part of your role. Additionally, you will be involved in executing Proof of Concept (POC) initiatives to test and validate proposed strategies.

10. Scalable MLOps Framework Development: Designing and developing scalable MLOps frameworks tailored to meet specific client requirements is a significant aspect of your responsibilities. This involves creating frameworks that streamline the deployment and management of machine learning models.

11. Collaboration with Data Science Team: Collaborating closely with Data Scientists and Data Engineers within the Data Science Team from project inception is essential. This collaborative effort ensures the alignment of machine learning initiatives with broader data science goals.

Qualifications:

To excel in this role, successful candidates should possess the following qualifications:

• Experience with Container Technologies: A strong background and hands-on experience with container technologies such as Docker, Kubernetes, EKS, and ECS is essential. This expertise ensures a solid foundation for deploying and managing machine learning models.

• Familiarity with Cloud Providers: Familiarity with cloud providers like AWS, GCP, and Azure is crucial. This includes the ability to leverage cloud services for scalable and efficient machine learning operations.

• Proficiency in Distributed Computing: Proficiency in distributed computing is a prerequisite for this role. Understanding the intricacies of distributed systems is essential for optimizing the performance of machine learning models at scale.

Preferred Tech Stack:

The preferred technology stack for this role includes:

• Programming Language: Proficiency in Python is essential, as it serves as a fundamental language for implementing machine learning algorithms and frameworks.

• MLOps Tools: Familiarity with MLOps tools such as MLFlow, Kubernetes, SQL, Docker, Bentos, Ray, Anyscale, and GuardRails is preferred. Experience with these tools enhances efficiency in managing the end-to-end machine learning lifecycle.

Join us in our mission to advance the field of Machine Learning and contribute to the development of cutting-edge solutions. This role offers a unique opportunity to work at the forefront of technological innovation and make a meaningful impact on the future of machine learning in our organization. If you are passionate about pushing the boundaries of what is possible in the field of ML and eager to collaborate with a dynamic team, we invite you to apply and be a part of our exciting journey.

Please click here to apply.