Indraneel M.

Key Skills

Software

AWS SageMaker

Other

Top Subject Matter

LLM prompt engineering and evaluation

Amazon Bedrock model onboarding and response evaluation

AI application data pipeline enablement for labeling workflows

Top Data Types

Text

Image

Top Task Types

Prompt + Response Writing (SFT)

Function Calling

Freelancer Overview

AWS prompt engineering and LLM customization learning (training/evaluation). Core strengths include AWS SageMaker and Other. Education includes Bachelor of Technology, ITM (SLS) Baroda University (2028) and Higher Secondary Education (Class XII), RMPS International School (2024). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including Prompt + Response Writing (SFT), Evaluation, and Rating.

Labeling Experience

Cloud labs supporting AI data flow (Pub/Sub, API Gateway, Looker)

OtherFunction Calling

Completed cloud-focused training covering services that support AI application pipelines, including Pub/Sub, API Gateway, and Looker. Practiced integrating these components in labs to enable data flow and downstream AI processing. While not explicit annotation work, the training supports building AI data collection and labeling workflows. • Get Started with Pub/Sub • Get Started with API Gateway • Get Started with Looker • Lab exercises across AWS and Google Cloud

2024 - Present

Amazon Bedrock getting started (AI model training/evaluation practice)

OtherText

Completed hands-on learning modules for Amazon Bedrock covering model onboarding and usage patterns. Used the training content to guide experimentation with prompt inputs and model outputs. Focus was on performing evaluation-oriented checks of responses during Bedrock learning. • Amazon Bedrock Getting Started • Prompt and model usage practice as part of onboarding • Applying Bedrock learnings to AI training-style experimentation • Guided exercises from GDG training

2024 - Present

AWS prompt engineering and LLM customization learning (training/evaluation)

AWS SageMakerTextPrompt + Response Writing (SFT)

Completed AWS prompt engineering and LLM customization training focused on preparing prompts and evaluating model behavior. Practiced using Amazon SageMaker JumpStart to run and assess LLM workflows. Applied the learnings to structured prompt/response tasks typical of AI training and evaluation. • AWS Foundations of Prompt Engineering • Customizing and evaluating LLM using SageMaker JumpStart • LLM fundamentals with emphasis on prompt design • Completion of related AWS LLM evaluation modules

2024 - Present

Education

Work History