Professional Overview
I hold a Master of Science in Computer Science from MDU Rohtak, along with an academic background in Electronics & Communication Engineering. Professionally, I currently work as an L2 Operations & Incident Support Engineer at Reliance Jio Infocom, with prior experience as an IBS Engineer at Value Connex and a Data Operator at SG Encon. As a native Hindi speaker who has completed technical education and professional documentation in English, I possess strong bilingual fluency in both languages. This linguistic foundation allows me to comprehend, evaluate, and communicate nuanced content effectively in both Hindi and English. Furthermore, my key technical skills include Python programming, data cleansing, and data annotation, enabling me to manage data-centric tasks with high accuracy.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built and deployed production-level LLM chatbots for customer support.
- Fine-tuned open-source LLMs using LoRA for domain-specific tasks.
- Designed and optimized RAG pipelines using vector databases.
- Built scalable backend AI microservices using Python and FastAPI.
- Automated model evaluation, prompt testing, and data validation flows.
Key Achievements / KPIs
- Reduced customer support response times by 45% using automated AI bots.
- Boosted RAG retrieval accuracy by 30% through semantic search tuning.
- Lowered inference latency by 35% via model quantization and caching.
- Handled 100k+ daily queries with 99.9% uptime and zero data leakage.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built and deployed production-level LLM chatbots for customer support.
- Fine-tuned open-source LLMs using LoRA for domain-specific tasks.
- Designed and optimized RAG pipelines using vector databases.
- Built scalable backend AI microservices using Python and FastAPI.
- Automated model evaluation, prompt testing, and data validation flows.
Key Achievements / KPIs
- Reduced customer support response times by 45% using automated AI bots.
- Boosted RAG retrieval accuracy by 30% through semantic search tuning.
- Lowered inference latency by 35% via model quantization and caching.
- Handled 100k+ daily queries with 99.9% uptime and zero data leakage.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built and deployed production-level LLM chatbots for customer support.
- Fine-tuned open-source LLMs using LoRA for domain-specific tasks.
- Designed and optimized RAG pipelines using vector databases.
- Built scalable backend AI microservices using Python and FastAPI.
- Automated model evaluation, prompt testing, and data validation flows.
Key Achievements / KPIs
- Reduced customer support response times by 45% using automated AI bots.
- Boosted RAG retrieval accuracy by 30% through semantic search tuning.
- Lowered inference latency by 35% via model quantization and caching.
- Handled 100k+ daily queries with 99.9% uptime and zero data leakage.
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
