Machine Learning Engineer

Job Role - Machine Learning Engineer

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, Statistics and Deep Learning
• Exposure to deployments and performance monitoring of statistical and machine learning models in production .
• Experience in object oriented programming and software development lifecycle.
• Exposure on time series Forecasting and Prophet time series model.
• Advanced math, probability and statistics knowledge, particularly in the areas of calculus, linear algebra, functional analysis and Bayesian statistics.
• 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 modeling and statistics for classification, clustering, forecasting, Time series modeling.
• 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, data engineers, software developers and business stakeholders, to ensure solutions meet business needs.
• Adhering to project timelines
• Communicate with non-technical stakeholders to understand their requirements and convey the benefits of the ML solutions

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.