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.

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