Machine Learning Engineer Lead
7 days ago
**Overview of the Role at Saras Analytics**
We are looking for an experienced AI/ML engineer to join our team at Saras Analytics. The successful candidate will be responsible for designing, developing, and implementing machine learning pipelines for automated workflows and customer insights generation.
This role involves working with cutting-edge AI advancements, including Retrieval-Augmented Generation (RAG) and AI Agents, to contribute to end-to-end automated Data Science solutions.
The selected candidate will be expected to automate workflows, transition proofs of concept (POCs) into production environments, and build scalable solutions such as customer segmentation, demand forecasting, and market mix models.
Responsibilities and Expectations:- Design, develop, and implement machine learning pipelines for automated workflows and customer insights generation.
- Build and optimize predictive models for applications such as demand forecasting, inventory forecasting, and market mix modeling.
- Develop natural language processing (NLP) modules leveraging techniques such as Retrieval-Augmented Generation (RAG) and AI Agents with LLMs.
- Deploy machine learning models into production environments, ensuring scalability, reliability, and efficiency.
- Collaborate with cross-functional teams, including data scientists, engineers, and stakeholders, to translate business needs into ML solutions.
**Requirements:**
- Bachelors or Masters degree in Computer Science, Machine Learning, Data Science, or a related field.
- Proven experience in machine learning engineering or a similar role.
- Proficiency in Python and its data science libraries.
- Hands-on experience with ML frameworks such as PyTorch and TensorFlow.
- Strong knowledge of data pre-processing, feature engineering, and model evaluation methodologies.
- Understanding of NLP concepts, including pre-processing, embeddings, entity recognition, and vector storage.
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