I'm Vishnu A C — a Software Engineer with 1.7 years of experience crafting scalable microservices, cloud-native architectures, and AI-powered solutions. Currently serving notice — available from May 18, 2026.
With 1.7 years at Anunta Technology, I architect production-grade backend systems at real-world scale. I've slashed API latencies by 42%, boosted database efficiency by 90%, and compressed release cycles from 2 days to under 2 hours.
Currently on notice period (LWD: May 18, 2026). Actively exploring opportunities in backend engineering, AI systems, and cloud architecture.
Built production-grade agentic AI assistants using graph-based AutoGen workflows and RAG with ChromaDB. Designed multi-agent collaboration pipelines for enterprise automation.
Developed microservices to monitor Azure Virtual Desktop resources. Reduced API latency by 42% and achieved 99.7% uptime.
Built Go microservices for real-time Azure resource tracking. Improved database write efficiency by 90%.
Modernized deployment pipelines using Go and RabbitMQ. Compressed release cycles from 2 days → under 2 hours.
Migrated cost analytics from Python to Go. Reduced analytics runtime from 3 min → under 100ms — a 1800× improvement.
Personal AI Assistant
Containerized local LLM assistant built with Go and Ollama. Features streaming responses, conversation memory, and complete data privacy — all running locally.
func StreamResponse(prompt string) {
client := ollama.NewClient()
stream := client.Chat(prompt)
for chunk := range stream {
fmt.Print(chunk.Content)
}
}ATS & Interview Engine
AI-powered resume scoring using sentence embeddings. Automates candidate screening with configurable scoring weights and semantic skill extraction.
model = SentenceTransformer("all-MiniLM")
resume_vec = model.encode(resume)
job_vec = model.encode(job_desc)
score = cosine_sim(resume_vec, job_vec)Legal RAG Chatbot
RAG-powered chatbot for Indian legal documents. Uses ChromaDB vector retrieval for accurate, context-aware legal assistance from a corpus of legislation.
collection = chroma.get_collection(
name="indian_law"
)
results = collection.query(
query_texts=[user_query],
n_results=5
)Confluence AI Companion
Enterprise AI assistant that connects to Confluence workspaces. Query documentation using natural language — search, summarize, and navigate team knowledge bases.
async def query_docs(q: str):
docs = await retriever.search(q)
ctx = build_context(docs)
return await llm.generate(
prompt=q, context=ctx
)ML Activity Monitor
ML-powered work activity monitor using Random Forest classifier. Detects and classifies user activity states in real-time for productivity insights.
model = RandomForestClassifier(
n_estimators=100,
max_depth=10
)
model.fit(X_train, y_train)
state = model.predict(live_data)I'm currently looking for new opportunities. Whether you have a question, a project idea, or just want to say hello — my inbox is always open.