Raj Agent OS Portfolio
Agent OS style portfolio and writing platform for AI engineering work, Claude Code field notes, live system previews, and privacy-first engagement logging.
Vibe Coder / Senior AI Developer at Accellor / IIT Mandi
Currently Senior AI Developer at Accellor, building RAG systems, multi-agent workflows, AI agents, and generative AI products.
I build AI systems that turn messy product intent into useful workflows: RAG products, multi-agent automations, analytics surfaces, and the glue code that helps teams ship faster.
I am currently a Senior AI Developer at Accellor, with prior data science work across RevSure AI, Pixis, and LEAD School.
Agent OS style portfolio and writing platform for AI engineering work, Claude Code field notes, live system previews, and privacy-first engagement logging.
Minimalist plain-text version of the portfolio for rajsharma.space with fast reading, direct links, resume, writing, and project summaries.
Markdown-first text workspace with diagram + math rendering.
Expense splitting + collaboration product built as a multi-repo system (core + UI + chat).
Chat + collaboration surface paired with SplitKar.
Utility app for streamlined expense workflows and summaries.
Marketing + documentation site for ActionHub with SEO-focused updates.
Real-time timekeeping utility app shipped end-to-end.
A set of minimal single-purpose sites demonstrating rapid shipping: invitations, plans, and quick static pages.
Exploration into agent-like analytical workflows combining reasoning with structured data steps.
Classic recommendation modelling foundations for personalization use cases.
Reserved entries for corporate work. Company name + timeline will be added once confirmed.
Current AI systems work: Current role, project details coming later.
Lead Fit scoring system: Prioritized accounts using firmographic, behavioral, and conversion signals to improve sales targeting efficiency.
Lead Propensity model: Predicted progression across the funnel (MQL→SQL→Opportunity→Closed-Won) to improve forecasting and campaign optimization.
Title Standardization: LLM + rule-based cleaning/normalization for CRM job title data to improve downstream model performance and reporting.
Probabilistic multi-touch attribution: HMM-based attribution to quantify channel impact and move beyond static rule-based attribution.
Buying Stage inference: Combined product, marketing, and sales signals to infer customer journey stages and provide real-time visibility.
LLM-based data standardization: Applied LLM-based semantic normalization techniques to reduce manual effort and improve data quality across pipelines.
Predictive targeting & optimization models: Behavioral, engagement, and contextual signal models to improve campaign performance.
Marketing data pipelines: Built real-time + batch processing pipelines using AWS S3 + Athena for ML inference and reporting.
Feature engineering framework: Reusable feature creation workflow to transform raw signals into ML-ready inputs.
Codeless AI infrastructure: Enabled non-technical users to leverage ML insights in workflows.
Early generative AI experiments: Prompt-based experimentation and integrations for targeting/segmentation automation.
Product dashboards & reporting: Built interactive dashboards to track performance and business metrics for product teams.
Reporting pipeline automation: Automated reporting workflows using Python + Airflow, reducing manual reporting effort.
Placement policy & stakeholder communication: Improved student placement experience and policy execution through coordination with academic departments and administration.
Crack detection POCs: Led POCs and problem-solving for crack detection using tooling and data interpretation.
Data quality & troubleshooting: Troubleshot technical issues to ensure accurate submissions and data integrity for contractors.
Ten habits that make Claude Code feel less random and more like a useful coding partner.
A full-stack workflow for planning, model choice, memory, review, hooks, skills, and parallel execution.
How to turn repeated reminders into Claude Code hooks that actually run.
Why reusable skills beat ad-hoc prompts for engineering workflows that need process.
A practical map of the slash commands worth building into Claude Code muscle memory.
How plan mode separates deciding what should change from writing the code.
A modern decision rule for choosing between prompt-like commands and structured skills.
Formal wording: Senior AI Developer at Accellor, Data Scientist at RevSure AI and Pixis, Product Analyst I at LEAD School.
The first chapter was rooted in Chauri, around my maternal grandparents' home. It gave me a grounded start: family, discipline, and the kind of curiosity that comes from watching people solve everyday problems with limited resources.
Primary schooling started in my village, Kanhaiyachak. It was a simple beginning, but it built the first habits of showing up, learning steadily, and staying close to the realities around me.
At Shanti Kunj Public School, the academic world widened. This was where school became more structured and I started building confidence across subjects.
Bal Vidya Niketan became the phase where maths and physics started standing out. The focus shifted from just doing well in class to preparing for a larger academic path.
The Super 30 phase in Patna sharpened everything: science, JEE preparation, independence, and the belief that a student from a small-town background could compete at a national level.
IIT Mandi gave me a technical foundation through Civil Engineering, plus a management minor and a wider world of projects, leadership, and problem-solving. It was also the bridge from academic strength into product and data thinking.
The professional chapter started with product analytics, moved into production data science and revenue intelligence, and now sits in agentic AI systems: RAG, multi-agent workflows, AI agents, and generative AI products.
Preferred channel: contact@rajsharma.space.