Job Description: Machine Learning Engineer RAG and Data Pipeline Development
Position Overview
We are seeking a skilled and experienced
Machine Learning Engineer to design and implement advanced data pipelines and ML models, with a focus on
Retrieval-Augmented Generation (RAG) and data cleaning techniques. The role demands strong expertise in machine learning, feature engineering, and cloud-based AI/ML tools to build robust and scalable solutions. The ideal candidate will have hands-on experience in leveraging
Google Cloud Platform (GCP),
BigQuery, and
Vertex AI for ML development and deployment.
Key Responsibilities
- Develop end-to-end ML pipelines, from data preprocessing and cleaning to model deployment and monitoring.
- Design and implement solutions leveraging RAG (Retrieval-Augmented Generation) for advanced data insights.
- Build and optimize recommendation systems, supervised learning models, clustering algorithms, and time series forecasting solutions.
- Conduct network science analysis to uncover actionable insights.
- Perform feature engineering to create meaningful datasets for ML models.
- Integrate insights from multiple datasets to enhance decision-making processes.
- Utilize GCP tools such as BigQuery, Vertex AI, and data analytics platforms for scalable ML solutions.
- Build interactive dashboards using Power BI or Google Looker to present insights effectively.
- Collaborate with cross-functional teams using Agile methodologies to ensure timely and quality delivery.
Qualifications And Skills
- Experience: Minimum 5-6 years of hands-on experience in machine learning, data pipelines, and related domains.
- ML Expertise: Proficiency in scikit-learn, TensorFlow, and other ML frameworks.
- Programming: Strong programming skills in Python, with additional knowledge of R or Scala being a plus.
- Feature Engineering: Proven expertise in creating effective features from diverse datasets.
- Cloud ML: Deep experience in Google Cloud AI/ML tools, particularly Vertex AI and BigQuery.
- Data Analytics: Experience with large datasets, data cleaning, and deriving actionable insights.
- Dashboarding: Familiarity with tools like Power BI or Google Looker for visualization.
- Soft Skills: Excellent communication and collaboration skills to work within Agile teams.
Preferred Skills
- Experience in network science projects.
- Familiarity with time series forecasting and recommendation systems.
- Understanding of Agile and Scrum methodologies for effective project delivery.
Skills: recommender systems,feature engineering,time series analysis,network science,python,google cloud platform,power bi
