NLP/ML Architect at Isentia
We are looking for a NLP/ML Architect to join the Isentia team in Sydney
As a NLP/ML Architect, you will be responsible for creating NLP/ML solution prototypes and working with our engineering team to transition these prototypes into production. You will work closely with the business and Tech teams to create machine learning platforms across various problem domains including but not limited to voice-to-text, object recognition and document clustering. You will also be a big driver in our ML/NLP roadmap.
We are looking for someone who is curious, a self-starter, continuous learner and progressive in nature as our team plays in a variety of spaces from microservices, NLP and Machine learning to continuous delivery and DevOps in the AWS cloud.
Our team works in an agile environment encompassing DevOps & CI/CD. We have multiple production workloads using ML and NLP already in the cloud and are continuing to grow!
- Research, design and evaluate NLP/ML models for identified problems
- Regularly report research outcomes to business and IT stakeholders
- Develop end-to-end content relevance and search solutions including similarity detection algorithms
- Identify key prediction, classification and clustering ML problems based on Isentia business domains
- Use informal problem statements, formal descriptions, success criteria and assumptions to define the identified problems
- Develop end-to-end NLP/ML solutions, and collaborate with technical architects to transition the prototype into the production environment
- Stay up-to-date on emerging NLP, ML and IR trends and research
Who are you?
- 5+ years’ experience working on ML problems in an industry with a large data set
- Research Master’s or PhD education
- Deep knowledge of Numpy, Scipy/SkLearn and Pandas
- Knowledge of ML techniques such as linear regression, logistic regression, SVM and decision trees
- Knowledge of DL and NLP concepts
- Great problem-solving skills
- Hands-on experience with DL libraries
- Confident expressing real-world problems in the ML space
- SQL, Java or AWS experience nice-to-have
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