Applied Machine Learning Engineer

Haven’t heard of us before? No problem. First off, it’s pronounced In-fur-d. We are the creators of a proprietary Intelligent Document Processing platform that automates data extraction from complex and messy unstructured documents. For over a decade, we’ve been building expertise in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Neural Networks and much more. We are building technology that is disrupting the data extraction space for 5+ years now.

And now, we are on the lookout for a motivated Applied Machine Learning Engineer .In this role, you will help us to build our product in an efficient and solid way. 

As a Applied Machine Learning Engineer at Infrrd, your job responsibilities will include:

• Translate business needs into technical solutions: Collaborate with stakeholders to understand complex business problems and define actionable machine learning solutions. 
• Develop and deploy scalable ML models: Design, build, and optimize ML models for production environments, considering factors like performance, scalability, and cost.
   
• MLOps expertise: Implement robust MLOps pipelines, including data ingestion, feature engineering, model training, deployment, monitoring, and retraining. 
• Optimize model performance: Continuously evaluate and improve model performance metrics, addressing issues like model drift and degradation. 
• Infrastructure and tools: Leverage cloud platforms (e.g., AWS, Azure) and ML frameworks (e.g., TensorFlow, PyTorch) to build and deploy ML solutions efficiently.
• Knowledge sharing: Conduct code reviews, and foster a culture of continuous learning within the ML engineering team.

Below is a list of the background we would like our Applied Machine Learning Engineer to have:

• BE or BTech in Computer Science Engineering, or a related discipline with a strong foundation in mathematics and statistics. Specialization in Artificial Intelligence or Machine Learning during the undergraduate program is a significant advantage. Postgraduate degrees (MTech or MS) in relevant fields are a plus.
• 1-3 years of expertise in Python programming.
• Familiarity with popular ML libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
• Proficiency in a variety of ML algorithms including linear regression, logistic regression, decision trees, random forests, support vector machines, and deep learning (CNNs, RNNs, transformers).
• Experience with data preprocessing, feature engineering, model evaluation, and hyperparameter tuning.
• Solid understanding of probability and statistical concepts, including hypothesis testing, Bayesian inference, and model evaluation metrics.
• Experience with generative and discriminative models, and their practical applications.
• Ability to break down complex problems, analyze data, and develop effective ML solutions.
• Excellent communication skills to articulate technical concepts to both technical and non-technical stakeholders.