AI Engineer with 2+ years in Prompt Engineering & Data Annotation
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AI and GenAI specialist with hands-on experience in prompt engineering, data annotation, and large language model evaluation. Skilled in designing effective prompts and improving model outputs through structured feedback and analysis. Committed to driving innovation in AI automation and contributing to impactful, next-generation solutions.
Deogiri Institute Of Engineering & Management Studies
B.TECH CSE · Computer Science Engineering
N/A – June 30, 2023
Vasantrao Naik Junior College
H.S.C.
N/A – May 31, 2019
Sanskar Vidyalaya
S.S.C.
N/A – May 31, 2017
Keywords Studios Limited
Technical Research Associate AI – Prompt Engineer
July 1, 2025 – Present
Bengaluru, Karnataka, India
Zensar Technologies
Junior Associate Process Executive - Prompt Engineer
October 1, 2024 – February 28, 2025
Pune, Maharashtra, India
Han Digital Solutions (P) Ltd.
Process Associate – Data Annotator
July 1, 2024 – September 30, 2024
Bengaluru, Karnataka, India
Insta-Human Management Pvt. Ltd.
Data Annotator
May 1, 2023 – February 29, 2024
Aurangabad, Maharashtra, India
AI Model Training: Workflow Automation & AI Interaction Training
June 25, 2026 – Present
Leverages Amazon's internal server tools to manage and preprocess large-scale datasets for training AI models to perform human-like interactions across web environments. Designs and executes step-level and complex-level prompts to create structured workflows that enhance AI accuracy, contextual understanding, and task completion efficiency. Applies advanced prompting methodologies, including Chain-of-Thought reasoning and Reinforcement Learning techniques, to improve model decision-making in dynamic web interaction scenarios. Evaluates model performance by analyzing user interaction data, identifying behavioral patterns, and optimizing outputs for better alignment with task objectives and user intent. Continuously refines data preprocessing, annotation pipelines, and workflow structures to improve dataset quality and model adaptability for complex web-based tasks.
Synthetic Session Review & Model Behavior Evaluation
June 25, 2026 – Present
Reviews AI-generated task execution videos to evaluate model behavior, workflow completion, and action accuracy against predefined task objectives. Assesses whether models successfully complete assigned tasks and verifies alignment between on-screen actions and narrated workflow descriptions for consistency and correctness. Identifies execution failures, narration mismatches, incomplete workflows, and anomalous behaviors, providing detailed feedback to improve automated evaluation pipelines. Applies advanced prompting methodologies, including Chain-of-Thought reasoning and Reinforcement Learning techniques, to improve model decision-making in dynamic web interaction scenarios. Contributes to improving AI evaluation frameworks by documenting performance insights and highlighting workflow-level issues impacting task success and model reliability.
AVX Gym QA & Capability Evaluation
June 25, 2026 – Present
Performs quality assurance and capability-focused evaluation of AVX Gym environments, testing model interaction across web-based scenarios involving drag-and-drop, hover interactions, downloads, UI responsiveness, and feature usability. Investigates seeded web environments to identify bugs, UX issues, enhancement opportunities, and redesign recommendations, documenting findings in structured feedback sheets for targeted capability improvement. Analyzes capability-specific workflows such as multi-level hover menus, drag-and-drop actions, file download behaviors, interactive UI components, and hidden/revealed controls to improve model testing coverage. Provides detailed feedback with issue classification (Bug Fix, Enhancement, UI/UX Redesign) and documents capability gaps to strengthen evaluation frameworks and scenario robustness. Collaborates with structured QA pipelines by validating task completion, recording technical observations, and improving gym environments for more effective AI capability assessment.
Visual QA Annotation
June 25, 2026 – Present
Created and annotated datasets with images, prompts, answers, and explanations to train multimodal AI models combining computer vision and NLP. Focused on labeling complex visuals like tables, charts, and diagrams to improve model performance in visual question answering tasks. Used SuperAnnotate to ensure precise annotations supporting ML training for better image understanding.
General Static Preference Collection (Prompt Rating)
June 25, 2026 – Present
Evaluated LLM outputs by comparing AI responses based on honesty, clarity, helpfulness, and format. Provided structured feedback to fine-tune prompts and enhance GenAI response quality. Managed the evaluation workflow using AWS SageMaker for efficient data processing and analysis.
Cultural Fit Analysis
The candidate's project diversity, ranging from workflow automation and AI interaction training to synthetic session review and visual QA annotation, indicates adaptability and a broad interest within the AI domain. Their experience across multiple companies, including Keywords Studios, Zensar Technologies, and Han Digital Solutions, suggests an ability to integrate into different work environments. The focus on improving AI accuracy, contextual understanding, and task completion aligns well with a results-oriented culture. However, the experience is heavily focused on annotation and prompt engineering, which might require further development in core AI/ML engineering for a senior AI Engineer role.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through their experience in leading a small team, managing task execution, troubleshooting, and optimizing team performance. Their project descriptions highlight collaboration with cross-functional teams and a focus on quality control, indicating good teamwork and attention to detail. The continuous refinement of data pipelines and evaluation frameworks suggests a proactive and problem-solving mindset.