Job Description

Technology name : Machine Learning , Statistics , Deep Learning , ML Ops , Data Analytics covering : NLP Linear Regression Classification & Clustering Transfer Learning NLP Forecasting based Models & Algorithms Large Language Models Machine learning frameworks such as Tensorflow, PyTorch, MLlib etc.
Sub-technology name: Python, SQL, Spark, Kubernetes, Jenkins, Prometheus
[Good to have Skills]: SQL, Data Analysis , Gaming Industry knowledge
Skills to be evaluated on : Machine Learning, Statistics, Deep Learning, Data Analytics
Mandatory Skills : Machine Learning, Statistics, Deep Learning, Data Analytics,
[Primary Skills]:

Design and develop various machine learning and deep learning models and systems for high impact consumer applications ranging from predictive safety, content personalization's, search, virtual assistant, time series forecasting and more.

Work with a broad spectrum of state-of-the-art machine learning and deep learning technologies, in the areas of various machine learning problems such as multilingual text classification, language modelling and multi-modal learning.

Create metrics and configure A/B testing to evaluate model performance offline and online to inform and convey our impacts to diverse groups of stakeholders.

Analyse and produce insights from a large amount of dynamic structured and unstructured data using modern big data and streaming technologies

Produce reusable code according to standard methodologies in Python, Scala or Java

Collaborate with cross-functional teams of technical members and non-technical members in architecture, design, and code reviews.
[Responsibilities and Duties]:
Lead dedicated team of software engineers to architect, build, deploy and support best-in-class software services that are always available to provide the best gaming experience for our customers.
Build and develop a high energy, committed, motivated engineering team focusing on engineering and operational excellence to deliver awesome business results.
Collaborate across business units and product teams to develop and execute against the team's vision, strategy, and roadmap.
Use technical expertise and industry trends to influence software development standard methodologies.
Handle day-to-day activities of engineering team using Agile/Scrum methodology.