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AI/ML Engineer with hands-on experience in multi-agent orchestration, RAG architectures, LLM-driven automation, and model fine-tuning. Skilled in Python, PyTorch, SQL, LangGraph, Docker, and AWS, with a foundation in building pipelines for document intelligence, agentic automation, and conversational AI. Known for end-to-end ownership, evaluation-driven thinking, and pragmatic problem-solving focused on shipping scalable, observable AI products. Published researcher with three accepted international papers.…AI/ML Engineer with hands-on experience in multi-agent orchestration, RAG architectures, LLM-driven automation, and model fine-tuning. Skilled in Python, PyTorch, SQL, LangGraph, Docker, and AWS, with a foundation in building pipelines for document intelligence, agentic automation, and conversational AI. Known for end-to-end ownership, evaluation-driven thinking, and pragmatic problem-solving focused on shipping scalable, observable AI products. Published researcher with three accepted international papers.

Thathsarani Sandarekhathathsaranisandare

AI Engineer, Data Scientist, Developer, +3

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AI/ML Engineer with hands-on experience in multi-agent orchestration, RAG architectures, LLM-driven automation, and model fine-tuning. Skilled in Python, PyTorch, SQL, LangGraph, Docker, and AWS, with a foundation in building pipelines for document intelligence, agentic automation, and conversational AI. Known for end-to-end ownership, evaluation-driven thinking, and pragmatic problem-solving focused on shipping scalable, observable AI products. Published researcher with three accepted international papers.…AI/ML Engineer with hands-on experience in multi-agent orchestration, RAG architectures, LLM-driven automation, and model fine-tuning. Skilled in Python, PyTorch, SQL, LangGraph, Docker, and AWS, with a foundation in building pipelines for document intelligence, agentic automation, and conversational AI. Known for end-to-end ownership, evaluation-driven thinking, and pragmatic problem-solving focused on shipping scalable, observable AI products. Published researcher with three accepted international papers.

Available to hire

AI/ML Engineer with hands-on experience in multi-agent orchestration, RAG architectures, LLM-driven automation, and model fine-tuning. Skilled in Python, PyTorch, SQL, LangGraph, Docker, and AWS, with a foundation in building pipelines for document intelligence, agentic automation, and conversational AI.

Known for end-to-end ownership, evaluation-driven thinking, and pragmatic problem-solving focused on shipping scalable, observable AI products. Published researcher with three accepted international papers.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer (Freelance) at Freelance (Remote)

January 1, 2024 - Present

Partnered with startups and AI-native teams to design and ship production LLM systems across multi-agent orchestration, document intelligence, fine-tuning, and LLM safety. Built production-grade agentic routing between NL→SQL and hybrid RAG (BM25 + vector + reranked) using LangGraph with RAGAS-based evaluation and SQL safety guardrails. Engineered a hierarchical multi-agent legal AI system with self-refinement loops and automated citation verification achieving a 0% fabrication rate in testing. Fine-tuned Llama 3.1 8B using LoRA and DPO, then optimized inference with AWQ 4-bit quantization and vLLM batch serving. Delivered defense-in-depth LLM safety for regulated FinTech use cases using prompt injection detection, PII redaction, schema enforcement, hallucination detection, and auditable evaluation across 500+ adversarial prompts via Langfuse tracing.

Machine Learning Engineer (Intern) at OCTAVE – John Keel ls Group

July 1, 2023 - October 1, 2024

Contributed to analytics and AI initiatives across hospitality and enterprise business units. At Cinnamon Hotels: helped build personalized offers & pricing systems, a Customer Lifetime Value prediction engine, and demand-elasticity-based pricing optimization for F&B and room products. Analyzed hotel energy usage by correlating occupancy, weather, and F&B revenue to identify optimization opportunities across 75% of total consumption. Supported food & beverage cost optimization (requisition standardization and interactive cost simulation dashboard) and integrated LLM-assisted reasoning into Azure Databricks pipelines using Azure OpenAI to reduce report generation time. Built 20+ PySpark/Databricks pipelines for dashboards and ML use cases, supported data quality engineering, statistical testing, and model development.