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/\ #\ \_____/ /# /\
/ \# \_.---._/ #/ \
/ /|\ | | /|\ \
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//\\ <\ _//^\\_ /> //\\
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class Harry(AIEngineer):
"""
"""
def __init__(self):
self.bio = """
"""
self.location = "Bhaktapur, Nepal"
self.phone = "+977 9814743551"
self.languages = {
"Maithili": "native",
"Nepali": "fluent",
"Hindi": "fluent",
"English": "intermediate",
}
self.education = [
{
"degree": "Data Science with Python Certification",
"school": "Mindrisers Institute of Technology",
"year": "2024-2025",
},
{
"degree": "Diploma in Computer Engineering",
"school": "Adarsha Secondary School",
"year": "2020-2024",
},
{
"degree": "Diploma in Graphic Designing",
"school": "New BIIT Institute",
"year": "2023",
},
]
self.experience = [
{
"role": "Jr. AI Engineer",
"company": "Mantra Ideas, Lalitpur",
"notes": [
"AI automation workflows",
],
},
{
"role": "Data Science Intern",
"company": "Mindrisers Technologies, Kathmandu",
"notes": [
"Generative AI solutions using LLaMA + LangChain",
"Customer churn prediction and sentiment analysis models",
"Deployed AI applications with the engineering team",
],
},
{
"role": "Volunteer — Birat Expo 2079",
"company": "Biratnagar, Nepal",
"year": "Dec 2022",
"notes": [
"System maintenance during the event",
"Event management support",
],
},
]
self.projects = [
{
"name": "Colait AI",
"type": "real-time voice-based AI interview platform",
"stack": "speech-to-speech via Amazon Nova Sonic + Strands agent framework on AWS Bedrock",
"highlights": "multi-mode prompt/tool architecture; custom Nepali TTS",
},
{
"name": "HelloSarkar (हेलोसरकार)",
"type": "Nepali-language voice AI system for government grievance registration",
},
{
"name": "Neplia",
"type": "Nepali small language model (SLM)",
"stack": "built on Qwen3-0.6B",
},
{
"name": "Real-time voice agent pipeline",
"type": "custom STT / LLM / TTS pipeline",
"stack": "LiveKit, Azure / Google Chirp3-HD / ElevenLabs / Groq",
"languages": "English, German, Nepali",
},
{
"name": "Nepali banknote CV",
"type": "denomination classification / retrieval",
"stack": "CLIP-based embeddings, few-shot learning",
},
]
self.skills = {
"programming": [
"Python", "NumPy", "Pandas", "Matplotlib", "Seaborn", "C++", "C",
],
"machine_learning": [
"Scikit-Learn", "Regression", "Classification", "Decision Trees", "Random Forests", "SVM", "KNN",
],
"deep_learning": [
"PyTorch", "TensorFlow", "Keras", "ANN", "CNN", "RNN", "LSTM", "Transformers",
],
"generative_ai": [
"LLMs", "LangChain", "LangGraph", "HuggingFace", "LLaMA", "OpenAI", "Groq", "RAG",
"Vector Databases (Pinecone, Chroma)",
],
"voice_pipelines": [
"LiveKit", "STT / TTS", "Amazon Nova Sonic", "ElevenLabs", "Google Chirp3-HD", "Azure",
],
"agent_frameworks": [
"AWS Bedrock", "Strands", "Google ADK",
],
"applied_cv": [
"CLIP-based embeddings", "few-shot learning",
],
"data_visualization": [
"Power BI", "Matplotlib", "Seaborn",
],
"databases": [
"SQL", "MongoDB", "Pinecone", "ChromaDB",
],
"tools": [
"Git / GitHub", "Docker", "Linux", "FastAPI", "Node.js / Express / Socket.io",
"Jupyter Notebook", "Google Colab", "VS Code", "Cursor",
],
}
def contact(self):
return {
"phone": "+977 9814743551",
"location": "Bhaktapur, Nepal",
}
if __name__ == "__main__":
harry = Harry()
print(harry.bio)
print(harry.contact())