Good. Now we switch mindset.
College placements ≠ Startup GenAI hiring.
For placements, recruiters care about:
- Strong fundamentals
- Clear internship
- 2–3 solid projects
- Achievements
- Clean formatting
- Not too “startup AI niche”
We make you look:
Strong Computer Engineering student with AI + backend exposure.
Not:
Hyper-niche GenAI infra engineer.
Below is your college placement optimized resume content.
This is clean, structured, safe for mass recruiters, product companies, and even service companies.
📄 COLLEGE PLACEMENT VERSION
SUMMARY
Computer Engineering student (PICT, 2027) specializing in backend systems, distributed IoT architectures, and performance engineering. Proficient in Rust, Go, Python, and C++, with hands-on experience building low-latency communication bridges (OCPP, ZeroMQ, MQTT) and high-throughput data pipelines (gRPC, TimescaleDB, Redis). Adept at orchestrating AI coding agents as a development velocity multiplier while owning system design, database schemas, and integration testing.
EDUCATION
Pune Institute of Computer Technology (PICT) — Pune
B.E. Computer Engineering | CGPA: 9.2 | 2023–2027
EXPERIENCE
Backend & Systems Engineer Intern — Golain
May 2026 – Present
- OCPP Rust Proxy Gateway: Engineered a high-performance EV proxy system in Rust for Enough Energy (UK) to connect physical charging stations with central software using the OCPP-J 1.6 protocol over WebSockets.
- Low-Latency Messaging: Designed a low-latency communication system using ZeroMQ (ZMQ) and MQTT to route real-time telemetry, implementing a smart local cache using Sled DB to prevent telemetry loss during network drops.
- AI-Agent Orchestration: Controlled AI coding workflows (using coding agents to generate boilerplate and test suites) to accelerate deployment speed while owning system design, database schemas, and integration testing.
Freelance Full-Stack Developer & Agent Operator
April 2026
- Telemetry Integration: Designed and built a student performance analytics dashboard for investor presentations, integrating physical pen-tech hardware telemetry with a backend database using React, TypeScript, and Firebase.
- AI Summarization: Built dynamic KPI charts, visualizations, and client-side AI summarizers to provide real-time strengths, weaknesses, and writing dynamics analysis.
- Human-in-the-Loop Design: Managed AI coding agents to rapidly scaffold UI components and routing, focusing manual cognitive effort on system schema, database design, and hardware-software data mapping.
AI Developer Intern — Banao.tech
Aug 2025 – May 2026
- EdTech Pipeline: Engineered backend components for an AI-driven video generation pipeline, automating personalized multilingual video creation from textual summaries and images.
- Serverless Telemetry: Deployed serverless workflows using AWS Lambda, implementing cloud logging and telemetry metrics with Python and LangChain to optimize execution and debug latency.
PROJECTS
Go, gRPC, TimescaleDB, Redis, MQTT, OpenTelemetry (OTLP)
- Developed a distributed industrial IoT telemetry ingestion backend in Go, utilizing gRPC microservices (Device, Telemetry, Alert) and TimescaleDB hypertables.
- Optimized database storage by implementing TimescaleDB time-partitioning, automatic 7-day compression (~90% savings), and retention policies.
- Integrated Redis for sensor telemetry deduplication and alert suppression, and instrumented all services with OpenTelemetry to trace across HTTP, gRPC, and MQTT boundaries.
PhotoStack AI (Intelligent Photo Gallery)
React, FastAPI, PostgreSQL, Google Gemini, Docker
- Engineered a private, local-first “Google Photos” alternative (PhotoStack AI) allowing natural language search queries on unstructured media.
- Implemented AI-driven semantic image tagging and text-to-SQL search, optimizing query performance for instant image retrieval.
PUBLICATION
GNN-Based Friend Recommendation: A Graph Embedding Approach for Social Networks
Accepted for Conference Presentation
- Compared supervised MLP and retrieval-based friend recommendation systems.
- Demonstrated scalability benefits of ANN-indexed retrieval while maintaining competitive accuracy.
- Designed graph embedding workflows for large-scale social network datasets.
TECHNICAL SKILLS
Languages: Rust, Go, Python, C++, SQL, TypeScript, JavaScript
Frameworks: FastAPI, React, Next.js, LangChain, ZeroMQ, gRPC
Cloud & Tools: AWS, Docker, Git, Linux, TimescaleDB, PostgreSQL, Redis, Firebase, OpenTelemetry (OTLP)
Concepts: Data Structures & Algorithms (DSA), Operating Systems (OS), Database Management Systems (DBMS), Computer Networks (CN), Object-Oriented Programming (OOP), Concurrency, System Design, IoT Protocols (OCPP, MQTT)
🎯 Why This Version Is Placement-Friendly
- Balanced AI + fundamentals.
- Clean internship impact.
- Research paper boosts profile strongly.
- No over-niche GenAI marketing tone.
- Shows backend + cloud + AI exposure.
- Includes core CS subjects (important for placements).