s
shakyaanuraj

Shakyadissayake

@shakyaanuraj
Sri Lanka
Englisch
Einige Informationen werden in englischer Sprache angezeigt.
Über mich
I'm a professional and experienced AI/ML engineer with 3+ years and 100+ project experience that comes with strong AI/ML academic background with reputed company experience. With hands-on experience in both academic research, corporate and freelance projects, I’ve built solutions ranging from Simple Chat Bots to Reinforcement Learning-powered robots. My technical stack includes Python, TensorFlow, PyTorch, Scikit-learn, OpenCV, Flask, and integrations with Hugging Face Spaces, among others. ... Mehr lesen

Kompetenzen

s
shakyaanuraj
Shakyadissayake
offline • 

Meine Dienstleistungen

Beratung für Datenwissenschaft
I will develop ai, ml, dl, llm and agentic ai solutions

Arbeitserfahrung

Associate Machine Learning Engineer

Augustory Corp • Vollzeit

Dec 2025 - Present5 mos

1. Collaborated with the Qimana (USA) engineering team to architect and deploy scalable AI systems in a distributed product environment, contributing to end-to-end feature delivery across research, backend, and data engineering workflows. 2. Engineered an Agentic AI framework integrating Knowledge Graphs and Graph RAG using Neo4j, enabling multi-hop reasoning over structured and unstructured enterprise data and improving contextual response relevance by ~30–40% compared to baseline vector-only retrieval. 3. Co-developed a real-time voice-to-voice intelligent interviewer agent using OpenAI APIs and WebSocket-based streaming, reducing response latency by ~35% and enabling seamless bidirectional conversational interactions for dynamic candidate assessment. 4. Designed and implemented a production-ready document extraction and ingestion pipeline leveraging Gemini Embeddings, LangChain semantic chunking, and agentic chunking with narrative-based story generation; optimized graph ingestion into Neo4j, increasing retrieval accuracy and reducing redundant node creation by ~25%. 5. Contributed to the architectural design and schema modeling of a FinTech Knowledge Graph for ledger account management, enabling structured financial relationship modeling, faster query execution (~40% improvement), and improved traceability of transaction flows across accounts. 6. Improved overall system robustness through iterative prompt engineering, retrieval optimization, and evaluation benchmarking, ensuring higher factual grounding and reduced hallucination rates in LLM-generated outputs.