Design and implement robust machine learning models and algorithms, focusing on recommendation systems.. Conduct data analysis to identify trends, insights, and opportunities for model improvement.. Collaborate with data scientists and software engineers to build and integrate end-to-end machine learning systems.. Optimize and fine-tune models for performance and scalability, ensuring seamless deployment.. Work with large datasets using SQL and Postgres to support model training and evaluation.. Implement and refine prompt engineering techniques for large language models (LLMs).. Stay current with advancements in AI/ML technologies, particularly in core ML algorithms like clustering and community detection.. Monitor model performance, conduct regular evaluations, and retrain models as needed.. Document processes, model performance metrics, and technical specifications.. Required Skills and Qualifications:. Bachelors or Masters degree in Computer Science, Data Science, or a related field.. Strong expertise in Python and experience with machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn).. Proven experience with SQL and Postgres for data manipulation and analysis.. Demonstrated experience building and deploying recommendation engines.. Solid understanding of core machine learning algorithms, including clustering and community detection.. Prior experience in building end-to-end machine learning systems.. Familiarity with prompt engineering and working with large language models (LLMs).. Proficiency with version control systems like Git.. Experience with cloud platforms (e.g., AWS) for model deployment and data storage.. Strong analytical and problem-solving skills.. Excellent communication and collaboration abilities.. Preferred Qualifications:. Experience with Graph DB (specifically Neo4J and cypher query language). Knowledge of large-scale data handling and optimization techniques.. Experience with Improving models with RLHF.