Help & Supportcareers@gravityer.com
GenAI Engineer | Building production RAG pipelines & LLM observability tools | MSc Advanced CS, University of Exeter
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
EvoTrace-3
March 7, 2026 – Present
Evolve Gemini's reasoning to get better answers using fewer tokens. An automated Genetic Algorithm for prompt and thinking-budget optimization.
View ProjectDocument-Intelligence-Toolkit
March 6, 2026 – Present
LangChain pipeline for PDF/DOCX ingestion, Map-Reduce summarization, structured LLM extraction, and semantic document comparison with a Streamlit UI.
View ProjectAgentForge
March 1, 2026 – Present
Multi-agent research engine powered by LangGraph + MCP + Claude. Planner → Researcher → Analyst → Writer → Critic pipeline that generates cited research reports from complex queries.
View ProjectGenAI-Product-Teardowns
March 1, 2026 – Present
Product teardowns of GenAI systems - reverse-engineering architecture, product decisions, and what I'd build differently
View ProjectLLM-Observability-Dashboard
February 21, 2026 – Present
An end-to-end observability dashboard for LLM applications built with FastAPI, Streamlit, Arize Phoenix, and Anthropic's Claude API. Monitor costs, latency, and quality metrics in real time.
View ProjectRAG-with-Evaluation-Pipeline
February 21, 2026 – Present
Production-grade RAG system with RAGAS evaluation harness, benchmarking framework, and experiment tracking to systematically measure and optimize LLM retrieval quality.
View ProjectN8N-Automations
November 16, 2025 – Present
A curated collection of ready-to-use n8n workflow templates aggregated from across the internet for easy discovery and quick deployment.
View ProjectCultural Fit Analysis
The candidate's project portfolio is heavily skewed towards Generative AI and LLM development, which aligns well with roles requiring innovation and exploration in this domain. The diversity of personal projects (e.g., I2S driver, GUI calculator) shows a broad technical curiosity. However, the lack of team-based projects or formal work experience makes it challenging to fully assess cultural fit in a collaborative, production-oriented environment. The candidate's experience level is 0, which might indicate a mismatch for a senior backend engineer role without further validation.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven individual with a strong interest in cutting-edge AI technologies. The focus on personal projects suggests a high degree of initiative and continuous learning. However, without formal work experience or psychometric test results, it is difficult to assess stress handling, team collaboration, or direct operational fit.