Airtel is hiring for a MLE professional who is passionate to work in the area of financial services (fintech) business.
Ever dreamed of working with 2-5 trillion records / day on a 200+ petabyte base?
Want to improve experience of ~400 million consumers using machine learning?
Do you want to work with one of world’s largest telco and India’s top brand?
If so, we would like you to join us on an exciting journey to create significant impact using data. The mission of the Data Science team is to transform terabytes of data into robust models for personalization and discovery.
Your work will directly impact the way millions of Indians use our Digital offerings. We would like you to join us on an exciting journey to create advanced ML products.
Responsibilities
1. Develop and deploy machine learning models with a focus on quick, reliable experimentation to validate features and hypotheses. Our team values speed and quality in experimentation.
2. Assist in building and maintaining machine learning pipelines, aiding in tasks such as data preparation, model training, and evaluation.
3. Create scalable and cost-effective inference pipelines using contemporary tools
4. Monitor the health and performance of machine learning models, to ensure their effectiveness
5.
Drive innovation in our Gen AI ecosystem by developing autonomous systems for key tasks like personalized recommendations, cross-sell and customer interactions
6. Contribute to building reusable ML/AI components, frameworks and best practices that enable deployment of solutions at scale.
7. Explore and build Agentic AI use cases, including AI agents capable of reasoning, planning, tool/API usage, workflow orchestration and autonomous execution of business tasks.
8. Be involved in technology research, capability building across newer technologies and tools in Machine Learning ecosystem at scale
Qualifications – As a successful applicant, you should have:
1. B.Tech or MS in Computer Science
2. Deep understanding of data structures and algorithms
3. Expertise in Python, modern ML frameworks (Pyspark, PyTorch, TensorFlow) and MLops.
4. Experience with large language models and prompt engineering
5. Experience optimizing model inference speed and cost for large-scale deployments
6. Proven track record in deploying production-ready ML models
7. Good understanding of Machine Learning algorithms
8. 3 to 6 years of experience in designing and deploying ML modeling and prediction pipelines
9. Ability to contribute to multiple projects simultaneously and work in a fast-paced, collaborative and iterative environment