AIML Scientist
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Job Location:
Monthly Salary:
Experience Required:
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
- Build train and deploy machine learning models to address key business challenges.
- Apply advanced deep learning techniquesincluding NLP computer vision and recommendation systemsusing frameworks such as TensorFlow and PyTorch.
- Design and develop scalable end-to-end AI pipelines from data preprocessing to production deployment.
- Leverage generative AI and large language models (e.g. transformers embedding) to deliver innovative solutions.
- Partner with product and engineering teams to integrate AI capabilities into business workflows seamlessly.
- Clearly communicate model results and insights to both technical and non-technical stakeholders.
- Proven experience in data science or applied machine learning
- Analytical-minded proactive and results-driven.
- Excellent communicator with the ability to simplify complex ideas for any audience.
- Quick to learn creative in problem solving and takes ownership.
- Takes ownership is self-motivated and thrives in a fast-paced environment.
Requirements
- 24 years of hands-on experience in data science or applied machine learning.
- Proficiency in Python and key machine learning libraries such as scikit-learn XGBoost and Hugging Face.
- Strong skills in data wrangling feature engineering and model evaluation.
Experience with generative AI and large language models (e.g. transformers embedding).
- Ability to build scalable end-to-end AI pipelines.
- Experience with cloud-based ML platforms (AWS GCP or Azure).
- Ability to stay current with emerging AI/ML trends and apply new techniques to solve real-world problems
Benefits
- Competitive salary and performance-based bonuses.
- Comprehensive insurance plans.
- Collaborative and supportive work environment
- Chance to learn and grow with a talented team.
- A positive and fun work environment.
Required Skills:
24 years of hands-on experience in data science or applied machine learning. Proficiency in Python and key machine learning libraries such as scikit-learn XGBoost and Hugging Face. Strong skills in data wrangling feature engineering and model evaluation. Experience with generative AI and large language models (e.g. transformers embedding). Ability to build scalable end-to-end AI pipelines. Experience with cloud-based ML platforms (AWS GCP or Azure). Ability to stay current with emerging AI/ML trends and apply new techniques to solve real-world problems
Company Industry
IT Services and IT Consulting
Key Skills
- Laboratory Experience
- Immunoassays
- Machine Learning
- Biochemistry
- Assays
- Research Experience
- Spectroscopy
- Research & Development
- cGMP
- Cell Culture
- Molecular Biology
- Data Analysis Skills
