While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

As a Machine Learning Engineer at Quantiphi, you will be responsible for designing and developing advanced machine learning models and algorithms to solve complex business problems. You will work on optimizing and deploying these models on AWS infrastructure, ensuring scalability and reliability.

Must have skills:

● 3+ years of experience on Python, SQL.
● Experience in object oriented programming and software development lifecycle.
● Exposure on Time Series Forecasting open source libraries (example prophet, darts, nixtla, gluonTS etc.)
● Advanced math,probability and statistics knowledge, particularly in the areas of calculus, linear algebra, and Bayesian statistics.
● Experience with Cross validation, backtesting, model selection, hyper-parameter fine-tuning, regularization, model calibration, monitoring model performance
● Technical experience implementing, and developing cloud ML models.
● Must have worked on version control systems such as Github and Code commit.
● Exposure of Docker containerization.
● Solve complex problems with multilayered data sets, and optimize existing machine learning libraries and frameworks
● Experience developing Predictive modelling and statistics for classification, clustering, Time series forecasting.
● Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness
● Experience on Pytorch - TensorFlow.

Role & Responsibilities :

● Experimenting with range of models, evaluating model performance and model selection.
● Performing data cleaning, feature engineering, selection and evaluation.
● Implementing the data and model training pipelines on cloud using AWS services such as sagemaker, lambda functions, etc.
● Documentation for Model architecture and solutions
● Collaboration with cross-functional teams, including platform engineers, Machine learning engineers, software developers and business stakeholders, to ensure data solutions meet business needs.
● Adhering to project timelines
● Communicate with non-technical stakeholders to understand their data requirements and convey the benefits of data solutions, including migration strategies

Good to have skills:

● Experience with Ensemble machine learning models
● Experience on building solutions on AWS architecture and exposure to AWS services (like Lambda, ECS, Airflow, Elastic search, etc..)
● Experience with AWS services such as Sagemaker for model training, Fine Tuning and Deployment.
● Relevant AWS certifications such as AWS Machine Learning speciality, AWS Solutions Architect.
● Experience with DevOps practices and continuous integration/continuous deployment (CI/CD) pipelines for data solutions.
● Implement and manage MLOps principles and best practices for machine learning models

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!