Qualifications
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Full time
Machine Learning Engineer
About Us
Whizzbridge is hiring a Mid to Senior Machine Learning Engineer. WhizzBridge is a technology solutions provider and a talent enabler. On one hand, it offers clients access to best of breed engineering talent and delivers their mission critical projects using the industry's best practices. On the other hand, it attracts and trains engineering talent on cutting edge technologies, programming languages and project management practices that set them up for successful professional and financial growth.
What We Offer
- Paid Leaves
- Medical Insurance
- Paid Udemy Courses and Certifications
- Career Progression Program
Job Description
- Design, train, evaluate and deploy machine learning models that solve defined client business problems.
- Frame ambiguous business questions as tractable machine learning problems, including selecting the right target variable and the right evaluation metric.
- Perform exploratory data analysis, feature engineering and data cleaning on client datasets, which are rarely clean on arrival.
- Build and validate models across supervised, unsupervised and time series problem types as the engagement requires.
- Establish rigorous evaluation methodology, including appropriate train and test splits, cross validation and guarding against leakage.
- Deploy models into production and monitor them for performance degradation and data drift.
- Communicate model behaviour, confidence and limitations to non technical client stakeholders in plain language.
- Work with domain specific model families where the engagement requires it, including natural language processing, computer vision or forecasting.
- Fine tune and adapt pretrained models where that is the appropriate solution, and recognise when it is not.
- Partner with platform engineers to ensure models are reproducible, versioned and supportable.
- Document methodology, assumptions and known limitations
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related field.
- Three or more years of applied machine learning experience with at least one model deployed to production and used by real users.
- Strong Python, with fluency in the scientific stack including NumPy, pandas and scikit learn.
- Hands on experience with PyTorch or TensorFlow.
- Strong SQL and comfort working with large datasets.
- Sound understanding of statistics and of evaluation methodology, including the ability to explain why a chosen metric is the right one for the problem.
- Experience with experiment tracking, such as MLflow.
- Ability to distinguish a model that performs well in a notebook from one that will survive production.
- Strong written and verbal English.
Qualifications
- Experience with Hugging Face Transformers and with fine tuning techniques including SFT, DPO or reinforcement fine tuning.
- Domain depth in natural language processing, computer vision, recommendation systems or time series forecasting.
- Experience with distributed training.
- Experience with model compression, quantization or inference optimization.
- Cloud machine learning platform experience on AWS, GCP or Azure.
- Kaggle competition results or published research.
- Experience working directly with external clients.