GenAI Developer

Roles and Responsibilities:
End-to-End ML Systems: Build, deploy, and maintain machine learning systems, including traditional ML models and Conversational AI, ensuring they operate at scale.
Statistical Modeling and Analysis: Research and develop innovative statistical and machine learning models for data analysis, identifying patterns, and improving decision-making processes.
RAG Applications: Develop and maintain Retrieval-Augmented Generation applications, implementing changes to algorithms to enhance AI performance and retrieval techniques.
Predictive Modeling: Use predictive modeling techniques to optimize customer service experiences and supply chain processes.
Custom Data Models: Develop custom data models and algorithms tailored to specific data sets, applying advanced statistical modeling techniques.
Collaboration: Work closely with data scientists, engineers, and other stakeholders to integrate ML models into production environments.
Continuous Improvement: Stay updated with the latest advancements in machine learning and AI, and continuously improve models and algorithms to ensure state-of-the-art performance.
Documentation: Maintain comprehensive documentation of models, algorithms, and system designs.

Mandatory Skills:
End-to-End ML Systems: Proven experience in building and deploying end-to-end machine learning systems, including traditional ML models and Conversational AI, at scale.
Problem-Solving: Strong ability to analyze and solve complex technical problems independently and collaboratively.

Preferred Skills:
RAG Applications: Experience in developing and maintaining Retrieval-Augmented Generation applications.
Statistical Modeling: Proficiency in statistical modeling techniques and data analysis.
Predictive Modeling: Experience in predictive modeling for customer service and supply chain optimization.
Programming Languages: Proficiency in programming languages commonly used in ML and AI development, such as Python, R, and Java.
Machine Learning Frameworks: Familiarity with ML frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
Data Analysis Tools: Experience with data analysis tools and platforms such as SQL, Pandas, and Hadoop.
Communication: Excellent verbal and written communication skills, with the ability to explain complex concepts to non-technical stakeholders.

Qualifications:
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
years of experience in machine learning, AI, and statistical modeling.
Proven track record of developing and deploying ML systems and applications at scale.
Strong problem-solving skills and ability to work both independently and in a team environment.