We have partnered with our NASDAQ listed client for enhancing their market leading products further
This role is the IT Solutions team
Roles and Responsibilities
The Machine Learning (ML) engineer will work with our specialist team to identify and explore various ML techniques to improve the current systems.
You will develop, test, deploy and document significant components in our services, critical for successfully shipping ML-driven features in our ITSM, Incident Management and other market leading tools
Key responsibilities
1. Identify reusable components in multiple ML services and take ownership of building, testing, and maintaining them end-to-end.
2. Work closely with the other engineers to improve the ML outputs' relevance and the robustness of the service (scalability, asynchronous processing, etc.).
3. Identify the right feature validation techniques and metrics required to monitor the feasibility and usability of the service.
4. Work closely with the designers and developers to plan the right experience and analytics required to perform user-validation.
5. Curate and collate validation datasets and perform validation studies periodically.
6. Produce technical documentation of all work
7. Communicate results of all ML work to the stakeholders of the services via periodic demos and presentations.
Required Skills
1. Well-versed in Python 3.x, including Python-based frameworks like Sanic, and Python concepts like worker management and asynchronous processing
2. Familiarity with AWS services - in particular, Lambda and Simple Queue Service (SQS)
3. Extensive experience with ML frameworks and libraries like PyTorch, TensorFlow, pyspark, scikit, Numpy, pandas.
4. Experience with the application of ML technologies like Language models, NLP, NLU, Anomaly detection, QA modeling, NERmodels, BERT, USE, SpanBERT, etc.
5. Must have worked with large data sets for fine-tuning and validation
6. Must have worked with Text analytics and aware of both supervised and unsupervised ML models with text analytics. Is well versed with various text classifications like Tf-Idf, Word2Vec, BERT etc
7. Understanding of NLP techniques for text representation, semantic extraction techniques, data structures and modeling
Additional Skills (Optional)
1. Previously worked with Transformers-based language models. Has experience fine-tuning these models and deployed/used them in a production setting.
2. Has worked with or knows graphs and knowledge-graphs
3. Has experience building an ML process end-to-end, beginning with problem formulation, experimentation to pipeline design, building model serving APIs, and deploying

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