Innodata

Profile URL

https://talent.gravityer.com/divyansh-bansal

Divyansh Bansal

Innodata

Gwalior, Madhya Pradesh, India

Since February 16, 2026

Skills

API IntegrationCertified Ethical Hackers (CEH)LinuxMERN StackNode.jsReact.jsAutomation TestingAutomationSoftware DevelopersData PreprocessingPostmanMysqlMongoDBAI Engineers

Experience

Innodata

AI Annotator

December 1, 2025 – Present

Frostrek AI

Prompt Engineer

November 1, 2025 – February 1, 2026

The Red Users

Cybersecurity Intern

March 1, 2025 – April 1, 2025

Tech Saksham (AICTE)

AI Intern

December 1, 2024 – February 1, 2025

India

IIT Kanpur

Cybersecurity Intern

June 1, 2023 – July 1, 2023

Kanpur, India

Key Strengths

Strong theoretical foundation in Artificial Intelligence and Large Language Models (LLMs), including Prompt Engineering, Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Retrieval-Augmented Generation (RAG).
Practical experience in prompt optimization, LLM output evaluation, and data annotation, directly aligning with modern AI/LLM development workflows.
Demonstrated ability to work on AI-driven projects, such as facial recognition systems and AI-based message routing, showcasing application of computer vision and NLP techniques.
Good understanding of data science principles as evidenced by the high score in the AI-specific technical assessment.

Skill Analysis

Data Science & Artificial Intelligence

★★★★★

94%

Achieved a very high score, indicating a strong grasp of fundamental and advanced concepts relevant to Data Science and Artificial Intelligence, particularly in the LLM domain as evidenced by resume.

data scientistMachine LearningLLMsPrompt EngineeringRLHFRAGModel Evaluation

Strengths

Excellent understanding of core AI and Data Science principles.
Strong theoretical knowledge in LLM concepts (Prompt Engineering, SFT, RLHF, RAG).
Proficiency in model evaluation techniques.

Limitations

Specific practical application gaps are not evident from this high score, but the general nature of the test means depth in specific frameworks or coding might not have been fully probed.

Python Programming & Data Analysis

★★★★★

31%

The low score highlights substantial gaps in fundamental Python programming, algorithmic thinking, and practical data manipulation skills, which are critical for an AI Engineer role.

Cultural & Operational Fit

Cultural Fit Analysis

The psychometric test score of 69% indicates an average fit regarding work attitude, stress handling, and team collaboration. While the candidate has experience collaborating in project teams, there is room for development to align with higher cultural fit expectations for a senior role.

Soft Skills & Operational Fit

AI is analyzing your overall score…

Identifying your key strengths…

Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Key Strengths

Strong theoretical foundation in Artificial Intelligence and Large Language Models (LLMs), including Prompt Engineering, Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Retrieval-Augmented Generation (RAG).
Practical experience in prompt optimization, LLM output evaluation, and data annotation, directly aligning with modern AI/LLM development workflows.
Demonstrated ability to work on AI-driven projects, such as facial recognition systems and AI-based message routing, showcasing application of computer vision and NLP techniques.
Good understanding of data science principles as evidenced by the high score in the AI-specific technical assessment.

Skill Analysis

Data Science & Artificial Intelligence

★★★★★

94%

Achieved a very high score, indicating a strong grasp of fundamental and advanced concepts relevant to Data Science and Artificial Intelligence, particularly in the LLM domain as evidenced by resume.

data scientistMachine LearningLLMsPrompt EngineeringRLHFRAGModel Evaluation

Strengths

Excellent understanding of core AI and Data Science principles.
Strong theoretical knowledge in LLM concepts (Prompt Engineering, SFT, RLHF, RAG).
Proficiency in model evaluation techniques.

Limitations

Specific practical application gaps are not evident from this high score, but the general nature of the test means depth in specific frameworks or coding might not have been fully probed.

Python Programming & Data Analysis

★★★★★

31%

The low score highlights substantial gaps in fundamental Python programming, algorithmic thinking, and practical data manipulation skills, which are critical for an AI Engineer role.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit