Headline

AI Engineer @ Growthack Ventures LLP | LLMs • RAG • AI Systems Architecture • FastAPI • Vector DBs • ML & MLOps

About

I’m an Applied AI Engineer focused on building end-to-end AI systems using Machine Learning and Generative AI. I have hands-on experience with: • LLM-based applications (RAG, FAISS, LangChain, Gemini) • Vector search and semantic retrieval systems • Deep learning models (LSTM, GRU) for time-series forecasting • Backend-integrated AI systems and deployment I’ve built real-world AI products such as an LLM-powered Equity Research Tool (RAG + FAISS) and deep learning stock market prediction models. I also have internship experience at Palo Alto Networks (AI + Cybersecurity) and AICTE (Full-stack AI development). 🎯 Actively seeking fresher / entry-level roles: AI Engineer • Applied AI Engineer • Generative AI Engineer • Software Engineer (AI) 📩 Open to referrals, collaborations, and technical discussions.

Location

Kharagpur

AI stack

antigravity

gemini

hugging face

langchain

langgraph

CrewAI

Work experience

AI Engineer (Generative AI & ML Systems) at Growthack Ventures LLP, Jun 2025 - Present · 1 yr 5 mos, Mumbai, Working as an AI Engineer focused on designing and building an AI-powered financial intelligence system. • Designed end-to-end system architecture for a Generative AI-based stock analysis platform using RAG (Retrieval-Augmented Generation) • Built a layered AI architecture integrating FastAPI backend, vector databases, and LLM pipelines • Implemented data ingestion pipelines for financial data (stocks, news, filings) with preprocessing and embedding generation • Developed RAG pipelines with embedding models, retrieval strategies, and prompt engineering to ensure grounded responses • Researched and defined scalable cloud infrastructure (GCP/AWS) for AI workloads and deployment • Designed system with SOC2-oriented security principles including encryption, access control, and audit logging • Planned future ML layer for predictive analytics (LSTM, XGBoost) with MLOps integration using MLflow • Worked on open-source tech stack selection including PostgreSQL, FAISS/Qdrant, Redis, Docker, and LangChain • Created modular, scalable, and production-ready architecture for AI-enabled financial ecosystem Currently working on system design, implementation planning, and scalable deployment strategy.

Python Full Stack Intern at All India Council for Technical Education (AICTE), Nov 2024 - Dec 2024 · 2 mos, Bhubaneswar, Odisha, India, Developed AI applications using Python and full-stack development concepts, building interactive AI-powered dashboards and deploying machine learning models as web-based solutions. • Built AI-powered web applications using Python and full-stack development concepts. • Developed interactive dashboards and deployed ML models as web-based solutions. • Integrated backend logic with AI workflows for real-world use cases.

Networks Cybersecurity Entry-level Technician (PCCET) Intern at Palo Alto Networks, Sep 2024 - Nov 2024 · 3 mos, Bhubaneswar, Odisha, India, • Implemented cybersecurity measures aligning with industry standards and NIST/NICE framework, enhancing organization's security posture. • Applied AI/ML security principles to protect systems against adversarial threats.

Education

Bachelor of Technology - BTech, Computer Science (AI/ML), School of Computer Engineering, KIIT DU

Public links

github: https://github.com/Indrasish7

indrasish-portfolio.web.app: https://indrasish-portfolio.web.app/