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Machine Learning Solutions Engineer
1 week ago
We are seeking an experienced AI professional to join our team as a Machine Learning Solutions Engineer. In this role, you will be responsible for designing, developing, and implementing machine learning models to address specific business needs. You will work closely with the Project Manager and AI Data Engineer to develop efficient AI-driven solutions.
Key Responsibilities:
- Design, develop, and implement machine learning models to address specific business needs.
- Optimize ML models through rigorous hyperparameter tuning and algorithmic enhancements.
- Develop and maintain MLOps pipelines to automate model training, validation, and deployment.
- Ensure proper model versioning and reproducibility using MLflow, SageMaker Pipelines, or Kubeflow.
- Build and maintain intelligent chatbot solutions, employing techniques such as:
• Retrieval-Augmented Generation (RAG):
• Improve response accuracy with relevant contextual data.
• Utilize Apache Tika for extracting and processing unstructured data (PDFs, text, documents, images).
• Clean, process, and transform raw datasets for efficient model training and inference.
• Deploy ML models on AWS SageMaker, Amazon Bedrock, and other cloud environments.
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Science, AI, Machine Learning, or a related field.
- 5–8 years of experience in AI/ML development, machine learning engineering, or software engineering roles.
- Strong understanding of ML models, fine-tuning, supervised learning, and deep learning techniques.
- Hands-on experience with generative AI (GenAI) and agentic AI.
- Experience deploying chatbot solutions using RAG, prompt engineering, and knowledge graphs.
- Proficiency in MLflow, SageMaker Pipelines, or Kubeflow for model versioning and tracking.
- Experience with model monitoring using SageMaker Model Monitor, AWS CloudWatch, or Prometheus.
- Strong experience in CI/CD for ML pipelines (GitHub Actions, Jenkins, or AWS CodePipeline).