Open to opportunities

Ashin Rajesh

@ashinrajesh

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AI prompt engineer specializing in LLM fine-tuning, RAG, and multi-agent automation for enterprise impact.

India

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What I'm looking for

I seek roles building production LLM systems and automation where I can apply fine-tuning, RAG, and multi-agent design to deliver measurable operational impact.

I am an AI prompt engineer and MBA+Computer Science graduate with three years of enterprise experience at HP and Accenture, focused on fine-tuning large language models and building production retrieval and automation systems that drive measurable outcomes. I fine-tuned Google Gemini via Vertex AI to raise knowledge-retrieval accuracy from 73% to 91% and implemented RAG architectures that cut query resolution time by 75%.

I design multi-agent workflows and no-code integrations to automate research and operational tasks, delivering 60% reductions in manual effort and 75% time savings in retrieval. I combine hands-on technical skills (Vertex AI, GPT-4, Claude, SQL, GCP/AWS) with client-facing program delivery—reducing incident resolution times, translating feedback into product features, and mentoring teams to accelerate onboarding.

Experience

Work history, roles, and key accomplishments

Technical Account Manager

Accenture

May 2024 - Oct 2024 (5 months)

Reduced incident resolution time 30% across 15+ enterprise clients and translated client feedback into 8 product features adopted into the core roadmap.

Root Cause Analysis Client Management Product Feedback Incident Management Roadmap Planning SLA Management

Technical Solutions Representative

HP Inc.

Nov 2020 - Sep 2023 (2 years 10 months)

Exceeded targets by 127% and achieved 96% satisfaction across 300+ accounts; optimized CRM workflows to reduce sales cycle 18% and improve forecast accuracy 22% while mentoring 12 members.

Sales Enablement Account Management Mentoring Forecasting Customer Satisfaction Process Improvement

AI Prompt Engineer

Ashinyx

Fine-tuned Google Gemini via Vertex AI to improve knowledge retrieval accuracy from 73% to 91% and deployed RAG and multi-agent workflows delivering 75% reduction in query resolution time.

Prompt Engineering Vertex AI RAG Architecture Vector Databases API Integration Chain Of Thought