MS_AG_AI_Backfill_IN_1

Key Responsibilities:

Hands-on Development:

• Develop and implement machine learning models and algorithms, including supervised, unsupervised, deep learning, and reinforcement learning techniques.
• Implement Generative AI solutions using technologies like RAG (Retrieval-Augmented Generation), Vector DBs, and frameworks such as LangChain and Hugging Face, Agentic AI.
• Utilize popular AI/ML frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
• Design and deploy NLP models and techniques, including text classification, RNNs, CNNs, and Transformer-based models like BERT.
• Ensure robust end-to-end AI/ML solutions, from data preprocessing and feature engineering to model deployment and monitoring.

Technical Proficiency:

• Demonstrate strong programming skills in languages commonly used for data science and ML, particularly Python.
• Leverage cloud platforms and services for AI/ML, especially AWS, with knowledge of AWS Sagemaker, Lambda, DynamoDB, S3, and other AWS resources.

Mentorship:

• Mentor and coach a team of data scientists and machine learning engineers, fostering skill development and professional growth.
• Provide technical guidance and support, helping team members overcome challenges and achieve project goals.
• Set technical direction and strategy for AI/ML projects, ensuring alignment with business goals and objectives.
• Facilitate knowledge sharing and collaboration within the team, promoting best practices and continuous learning.

Strategic Advisory:

• Collaborate with cross-functional teams to integrate AI/ML solutions into business processes and products.
• Provide strategic insights and recommendations to support decision-making processes.
• Communicate effectively with stakeholders at various levels, including technical and non-technical audiences.

Required Skills:

• Extensive proficiency with generative AI technologies, including RAG, Vector DBs, and frameworks such as LangChain and Hugging Face, Agentic AI.
• Proficiency in machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Extensive proficiency with AI/ML frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
• Strong knowledge of natural language processing (NLP) techniques and models, including Transformer-based models like BERT.
• Proficient programming skills in Python.
• Proficiency with cloud platforms like AWS, including AWS Sagemaker, Lambda, DynamoDB, S3, and other AWS resources.
• Proven ability to lead a team of data scientists or machine learning engineers on complex projects.
• Strong project management skills, with the ability to prioritize tasks, allocate resources, and meet deadlines.
• Excellent communication skills and the ability to convey complex technical concepts to diverse audiences.
• Strong problem-solving and analytical abilities.
• Proven ability to manage projects and activities.

Preferred Skills:

• Proficiency in setting technical direction and strategy for AI/ML projects.
• Knowledge of the Insurance domain.
• Ability to mentor and coach junior team members, fostering growth and development.
• Proven track record of successfully managing AI/ML projects from conception to deployment.