HRISHIKESH SHINGOTE
Generative AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Generative AI Engineer with less than a year in RAG pipelines & agentic workflows.
GenAI Engineer with hands-on experience building production-grade RAG pipelines, agentic AI workflows, and LLM-powered applications. Proven ability to design and deploy scalable AI systems leveraging LangChain, LangGraph, and modern microservices architectures. Delivered fraud detection solution in the BFSI domain with 95% precision, demonstrating expertise in translating complex AI concepts into production-ready systems. Strong foundation in secure API design, vector databases, and cloud-native deployments.
SMT Indira Gandhi College of Engineering
Bachelor of Engineering · Computer Science (AI/ML)
August 1, 2021 – June 30, 2025
SMT Sushila Devi Deshmukh Vidyalaya & Jr College
HSC
June 1, 2019 – May 31, 2021
Infosys
AI & Data Science Intern
October 1, 2024 – December 1, 2024
India
Multi-Agent Research Assistant with LangGraph
June 1, 2026 – Present
Built an agentic AI workflow with LangGraph for orchestrating multi-step research tasks with autonomous tool selection and query decomposition capabilities. Implemented DAG-based agent orchestration with conditional routing, web search integration, and synthesized response generation via LLM reasoning. Deployed RESTful API endpoint with FastAPI.
Production RAG System for Document Intelligence
June 1, 2026 – Present
Architected end-to-end RAG pipeline with PDF ingestion, semantic chunking via recursive text splitters, and vector search with pgVector and Chroma DB for enterprise knowledge retrieval. Optimized retrieval quality through LLM-based query enhancement, contextual compression, and hybrid search (dense + sparse vectors), reducing irrelevant context by 40%. Deployed secure REST API with FastAPI with comprehensive error handling for production readiness.
LLM-Powered Semantic Book Recommender
June 1, 2026 – Present
Developed a semantic recommendation system with OpenAI embeddings and vector similarity search for context-aware book recommendations based on plot summaries and user preferences. Implemented modular pipeline architecture enabling scalable integration of additional domains (movies, research papers) with minimal code changes.
Python Programming Certification
Unknown
June 1, 2026 – Present
Prompt Engineering & Programming with OpenAI
Columbia
June 1, 2026 – Present
OCI Generative AI Professional
Oracle
January 1, 2025 – Present
OCI AI Foundation Associate
Oracle
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's projects are highly relevant to the Generative AI Engineer role, showcasing a strong interest and practical application in the field. The diversity of projects (multi-agent systems, RAG, recommenders) and the internship experience indicate a proactive and hands-on approach to learning and applying AI technologies. The certifications further align with the target role, demonstrating a commitment to continuous learning in AI. However, the candidate is still pursuing a Bachelor's degree, which might indicate a more junior cultural fit in terms of extensive industry experience, despite advanced project work.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to design and implement complex AI solutions, suggesting strong problem-solving and analytical skills. The internship experience at Infosys demonstrates practical application in a corporate setting, including deploying production-ready systems and optimizing data pipelines. The focus on 'secure API design' and 'comprehensive error handling' points to an understanding of operational best practices. However, without direct behavioral assessment, specific soft skills like teamwork, leadership, or adaptability cannot be fully evaluated.