AI research & engineering for enterprise systems.
Lead AI Engineer specializing in Retrieval-Augmented Generation, distributed backend systems, and strict schema validation for high-stakes enterprise environments.
Technical Arsenal
AI, Systems & Observability
Core AI & Orchestration
Observability & Evals
Fine-Tuning & Inference
Vector Search & Data
Distributed Systems
Frontend Engineering
Professional Experience
BloomX Analytica
- ■Defined the end-to-end AI technical roadmap and architected production-grade LLM/RAG pipelines using Agentic AI patterns.
- ■Led foundation model evaluation and authored company-wide responsible AI standards covering data privacy and fairness.
B&S
- ■Owned the frontend architecture for a major European e-commerce platform, driving full-stack performance overhauls and API optimizations.
- ■Pioneered the company's first LLM-powered semantic search and shipped complex production-grade React.js UIs.
Wiley Sri Lanka
- ■Core engineer on a global e-learning LMS platform, building scalable full-stack features using PHP and Django.
- ■Acted as a key technical resource for distributed cross-functional squads, ensuring high-quality, well-tested code delivery.
M.SaaS
- ■Led frontend development for the company's core B2B SaaS product, building performant UI modules using React.js.
- ■Collaborated with product teams to translate design specs into accessible interfaces and introduced component-level unit testing.
Open Source
adaptive-rag-engine
PythonCloud-Ready AI Architecture: Engineered a FastAPI-based retrieval pipeline integrating FAISS and BM25, designed for seamless deployment onto AWS EC2/ECS instances to deliver scalable LLM grounding.
View Repositorylangchain-multi-agent-workflow
PythonAgentic Orchestration Framework: Architected a multi-agent AI system capable of decomposing complex user intents into specialized sub-tasks, mimicking a specialized cross-functional human team.
View Repositorycustomer-support-agent
PythonAutonomous Support Agent: Developed an intelligent customer support agent integrated with LangSmith for enterprise-grade observability, trace debugging, and continuous performance monitoring.
View Repository@spriyad
Hugging FaceExploring and deploying open-source LLMs, embedding models, and custom fine-tunes for enterprise RAG pipelines and agentic workflows.
Pragmatic RAG:
My Architectural Blueprint
Engineering for context and truth. This is my proven methodology for building enterprise-grade Retrieval-Augmented Generation systems that eliminate hallucinations and scale securely, following strict industry best practices.
Data Ingestion & Parsing
Most RAG fails at the PDF parser, not the LLM. I build robust extraction pipelines that preserve document structure—tables, headers, and metadata—before chunking even begins. If context is lost here, the vector database cannot save it.
Semantic Chunking & Embedding
Static token limits destroy meaning. I implement semantic chunking strategies that respect natural breakpoints in the data. Combined with high-dimensional embeddings, this ensures retrieval grabs complete thoughts, not broken fragments.
Vector Database Architecture
I design isolated namespaces and hybrid search indices (dense vector plus sparse BM25) to guarantee low-latency, high-precision retrieval across massive, unstructured enterprise datasets.
Query Rewriting & Routing
Users rarely ask perfectly formulated queries. I place a lightweight routing and query-expansion layer before the database to translate messy human intent into optimized semantic searches, drastically improving recall.
Context Injection & Generation
The final prompt is a strict schema. I enforce hard boundaries between the retrieved facts and the system instructions to prevent prompt injection and force the target LLM to synthesize answers strictly grounded in the retrieved truth.
Surgical Fine-Tuning:
Maximum Impact, Minimum Compute
My methodology for adapting open-weights models. By leveraging Parameter-Efficient Fine-Tuning (PEFT) and rigorous data curation, I deliver specialized AI capabilities that outperform massive generic models at a fraction of the inference cost.
* Illustrative metrics demonstrating the typical ROI of targeted LoRA adapters on domain-specific tasks compared to zero-shot prompting on base 8B-70B parameter models.
Dataset Curation & Formatting
Data quality beats volume every time. I spend the majority of fine-tuning cycles deduplicating, filtering noise, and formatting golden conversational pairs. A thousand perfect examples will always out-train a million mediocre ones.
Base Model Selection
I benchmark open-weights models against the specific domain task, balancing the required reasoning capabilities against the actual hardware budget and latency requirements of the target deployment environment.
Parameter-Efficient Fine-Tuning
I use LoRA and QLoRA to train targeted adapters on specific behaviors without catastrophically forgetting the base model's general knowledge. This allows for rapid experimentation and swappable domain expertise at runtime.
Evaluation & Benchmarking
Standard benchmarks rarely reflect production reality. I build automated LLM-as-a-judge pipelines and domain-specific regression tests to ensure the model solves the business problem without degrading baseline safety guardrails.
Deployment & Inference Optimization
An accurate model is useless if it is too slow. I deploy using continuous batching and KV-cache optimization engines to maximize GPU utilization and slash Time-To-First-Token.
System Architecture
& MLOps Ledger
I don't just build models on my laptop; I deploy them securely to millions of users. This is the production-grade stack I use to manage the entire machine learning lifecycle at scale.
Data Ingestion & Processing
High-throughput, fault-tolerant pipelines for structured and unstructured enterprise data.
Training Infrastructure
Distributed multi-node cluster orchestration for large-scale model pre-training and fine-tuning.
Model Registry & Tracking
Strict version control, experiment tracking, and artifact management for reproducible AI.
Serving & Inference
Ultra-low latency, high-throughput model serving with continuous batching and KV-cache optimization.
Business Impact
& Engineering Leadership
I build AI that makes money, saves time, and elevates the developers around me. A Lead Engineer bridges the gap between the engineering team and the executives, focusing on measurable ROI and strategic execution.
Cloud Compute Optimization
Reduced cloud compute costs by 45% ($1.2M ARR) by migrating from proprietary LLM APIs to self-hosted, quantized Llama-3 models via vLLM on spot instances, without degrading response quality.
Search & Retrieval Accuracy
Increased retrieval precision by 32% and downstream user conversion by 18% through the implementation of a hybrid dense-sparse RAG architecture with cross-encoder reranking.
Team Leadership & CI/CD
Mentored a team of 6 junior-to-mid level engineers, establishing strict CI/CD pipelines for ML models and reducing deployment failure rates from 15% to near-zero.
Strategic Execution
Bridged the gap between executive product vision and engineering execution, translating ambiguous business requirements into deterministic, scalable AI system architectures delivered ahead of schedule.
"One of the most reliable and skilled front-end developers I've worked with."
I've had the pleasure of working closely with Sathiska on several projects. Sathiska played a key role in building a strong technical foundation that allowed us to onboard many new businesses smoothly and efficiently. Beyond their technical strengths, what really stood out was Sathiska's dedication, excellent communication, and team-first attitude.
Daniël Laan
Development Manager IT - E-commerce"An intelligent and experienced software engineer."
Sathishka is a dedicated teammate and excellent frontend and UI engineer. He takes up challenges with much positive attitude and always help his other teammates. It was a pleasure to work with him.
Buddhini Perera
Program Manager"I was really impressed with his quality of work and attitude."
Sathishka Is always keen to learn and take challenges. His eagerness to learn new things has helped him master quite a few technologies quickly. He has been a great team player and has been trustworthy. I would highly recommend him for any organization.
Waruna Viraj Perera
Senior Front-End DeveloperData Dictionary &
Compliance Mapper
Project Overview
A live, deployed instance of a schema intelligence engine built to solve a critical enterprise bottleneck: identifying PII and mapping compliance rules across massive, undocumented SQL databases. Upload a raw DDL file or use the sample schema to watch the model process, structure, and tag the data in real-time.
Architecture Highlights
PII Detection Engine
Real-time schema parsing to automatically detect and classify Personally Identifiable Information.
Regulatory Mapping
Aligns raw database structures with GDPR and CCPA regulatory requirements dynamically.
Zero-Trust Processing
Secure, client-side processing pipeline ensures sensitive DDL schemas never leave the browser.