Rushika bijode
Open to work0 years experienceLLM Prompt and Backend Engineer
LLM Prompt and Backend Engineer | Computer Science Student
Standing Rank
Rank Not Available
Developer Badges
No badges earned yet
Skills & Technologies
52 skillsWork Experience
LLM Prompt and Backend Engineer Intern
Ethara AI
- Designed and optimized Retrieval-Augmented Generation (RAG) pipelines using LangChain, LlamaIndex, and FAISS, increasing context retrieval accuracy by 40% for legal document parsing.
- Fine-tuned open-source LLMs (Llama 3, Mistral-7B) using PEFT/LoRA parameter-efficient techniques, boosting classification F1-scores by 15% on customer service routing tasks.
- Built and scaled high-performance REST APIs with Python and FastAPI, reducing inference response latency by 30% through redis-backed prompt caching and asynchronous workflow design.
- Implemented LLM response validation and guardrails using Ragas frameworks to check and log semantic hallucinations.
- Containerized microservices using Docker and established GitHub Actions CI/CD to automate cloud deployments.
Projects
AgentOrch — Multi-Agent Orchestration Platform
- Architected a distributed multi-agent system coordinating Research, Synthesis, and Critique agents using LangGraph’s stateful cycle management.
- Engineered bi-directional messaging using FastAPI and WebSockets to stream intermediate LLM thought-steps and execution logs to the client.
- Developed an interactive React dashboard to visualize DAG execution graphs, task hierarchies, and streaming agent outputs.
DocuQuery — Hybrid RAG Search Engine
- Developed a document QA engine employing Semantic Chunking and a hybrid retrieval approach combining dense vector embeddings and BM25 lexical search.
- Integrated the Cohere Re-ranker API into the context retrieval flow, filtering candidate text passages and reducing prompt token payloads by 50%.
- Built a Streamlit frontend enabling corporate users to upload multi-page documents and retrieve grounded answers with precise page-level citations.
AI-EvalOps — LLM Observability Pipeline
- Designed a continuous evaluation pipeline integrating LangSmith to trace nested agent steps, execution latency, and usage costs.
- Automated daily benchmarking using the Ragas framework, calculating faithfulness and context recall metrics to preemptively log hallucinations.
- Created a monitoring dashboard indicating real-time cost analytics and prompt performance regressions across system updates.
Leaderboard Standings
Leaderboard Position Pending
Global test scores, peer standing percentiles, and algorithm leaderboard ranks are updated dynamically.
Assessment Highlights
Assessments Not Completed
Coding evaluations, system assessment results, and conceptual score badges will appear here after taking a test.
AI Collaboration Score
AI Collaboration Score Pending
Developer coding behavior, assistant cooperation, and AI pair-programming indicators are evaluated during live coding sessions.
Role Compatibility Profile
Role Compatibility Analysis Pending
Custom matching reports, candidate role compatibility percentiles, and core engineer strength profiles are processed once conceptual code screenings are complete.
Achievements
LeetCode
Solved 250+ coding problems focusing on Data Structures and Algorithms; active contest participant.
Databricks Certified Generative AI Engineer Associate
Certified in building, deploying, and evaluating robust GenAI systems on scale.
DeepLearning.AI and LangChain Certification
Certified in Advanced AI Agentic Architectures and RAG Systems.
AWS Certified Solutions Architect – Associate
Certified in designing secure, robust, and cost-effective cloud architectures.
IBM Generative AI Professional Certification
Certified in prompt engineering, RAG, and LLM orchestration.
Oracle Certified Professional, Java SE Developer
Specialized in Java concurrency, object-oriented design patterns, and JDBC.
Leadership
Head of Event Management, Vitronix Club; Event Coordinator, Cyber Warriors Club (VIT).
About Details
Professional Bio
LLM Prompt and Backend Engineer with expertise in RAG pipelines, fine-tuning LLMs, and building scalable REST APIs. Experienced in multi-agent orchestration, AI observability, and cloud deployment. Passionate about developing robust, high-performance GenAI systems.
Bachelor of Technology in Computer Science and Engineering in Computer Science and Engineering
Vellore Institute of Technology (VIT) (2022 - 2026)