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.