Machine Learning Engineer

Key Skills

Java Programming, Design of Experiment, Python Programming, Django, Framework, SQL, Git, AWS, REST API, NoSQL, Docker, Kubernetes-K8s, Grafana, SVN, Flask, SPARK Programming,

Job Description

• As an online platform, we aim to understand our users in order to help them in their customer journey. Part of this effort includes understanding the different learner and educator segments and their preferences. Your responsibility will be to drive such projects from the data science side
• Work on optimizing the models according to the requirements of the specific use case and
• collaborate with other team members to bring in production and scale machine learning

solutions.

• Measure the impact of your models on key metrics of the company.
• Collaborate across the Data Science community to find technical solutions to complex
• business problems.
• Work in an agile environment to deliver high quality software against aggressive

schedules.

Requirements

• 4 to 8 Years of relevant experience in Industry.
• Excellent working experience in Python.
• Knowledge of at least one Python web framework like Django/Flask. Preferable Django.
• Knowledge of big data and distributed computing, Data Pipelines etc.
• ML Model deployment and performance optimization
• Good hands-on experience of implementing REST APIs.
• Good understanding of microservice architecture.
• Strong understanding of relational and NoSQL databases.
• Strong understanding of data structures and algorithms.
• Good understanding of design principles.
• Hands-on experience of AWS services.
• Knowledge of version control tools like Git/SVN.
• Strong unit testing and debugging skills.

Good To Have:

• Understanding of Kubernetes, Docker, helm-charts.
• Understanding of terraforming or ansible.
• Prior experience on deployment/infrastructure automation
• Experience bringing models in production.
• Industry experience creating and productionizing machine learning algorithms at scale (e.g., production use of AWS Athena, AWS Sagemaker, AWS Spectrum, AWS Kinesis) or the GCP equivalent.
• Experience setting monitoring (e.g., Grafana) and alert notifications (e.g., New Relic), etc.
• Experience building large-scale batch and real-time data pipelines with open source data processing frameworks like Spark or Storm.