INTELLIGENCE BRIEFING · VOLUME I · N° 42
EST. 2024COLOMBO, CEYLON · REMOTE
JEGAN.T
AI ENGINEER AVAILABLE
— FRONT PAGE · HEADLINE STORY · COLOMBO, CEYLONAI ENGINEERING · LLMs · COMPUTER VISION · MLOps · AGENTIC SYSTEMS

MACHINESDON'T THINK.ENGINEERSDO.

Filed byJEGAN.T· AI ENGINEER
Filed04 Projects
Dispatches03 Posts
Last EditAPR 2026
StatusOn Press

— LEAD ARTICLE

The most dangerous assumption in AI is that the model is the product. It isn't.

The system around it — the data pipelines, the evaluation harness, the deployment strategy — that's the product. The model is just a component.

I build AI systems that are actually deployed — not just impressive in notebooks. My work spans LLM application development, computer vision pipelines, and the infrastructure that keeps both running in production.

Engineering intelligence means understanding where models fail, and building for the humans on the other end.
CURRENT FOCUSProduction LLM systems with agentic reasoning.
AVAILABILITYOpen to full-time and contract roles.
LOCATIONRemote-first. Based in Sri Lanka.

PROJECTS

ENGINEERING REGISTER · 4 FILES · 3 IN PRODUCTION
§ SECTION I
FILE № 001 · PUBLIC● DEPLOYED
★ LEAD STORY

LLMs & GenAI Platform

A real-time document intelligence pipeline built on LangChain and GPT-4. Extracts structured insights from unstructured enterprise data, routes queries to specialized sub-agents, and delivers auditable reasoning chains to end users.

  • 01Reduced document review time by 68% across a team of 30 analysts
  • 02Processes 10,000+ documents per day with P95 latency under 3 seconds
  • 0399.2% uptime over 6 months of production operation
  • 04Sub-agent routing achieves 91% task decomposition accuracy on held-out eval set

TECHNICAL INDEX

PythonLangChainFastAPIOpenAIPineconeRedis
Read the Full File→
FILE № 002 · PUBLIC● DEPLOYED

Computer Vision Pipeline

An end-to-end computer vision system for real-time object detection and classification. Trained on custom datasets, optimized for edge deployment, and integrated with a monitoring dashboard for production drift detection.

  • 01Achieves 94.3% mAP on custom detection task, up from 71% baseline
  • 02Inference latency of 28ms on NVIDIA Jetson at production resolution

TECHNICAL INDEX

PythonPyTorchYOLOOpenCVFastAPI+ 1
Access File→
FILE № 003 · PUBLIC● DEPLOYED

MLOps Infrastructure

Full-stack MLOps platform handling model versioning, experiment tracking, automated retraining triggers, and canary deployments. Cut deployment cycle from 3 days to 4 hours across a team of 8 engineers.

  • 01Deployment cycle cut from 3 days to 4 hours (18× improvement)
  • 02Automated retraining on data drift reduced model staleness incidents by 80%

TECHNICAL INDEX

MLflowAirflowKubernetesTerraformAWS+ 1
Access File→
FILE № 004 · PUBLIC◌ IN PROGRESS

Autonomous Research Agent

A multi-agent system that autonomously plans, researches, and synthesizes technical reports. Combines web search, code execution, and structured output to deliver analyst-grade summaries on demand.

  • 01Generates analyst-grade 2,000-word reports in under 4 minutes
  • 02Planning accuracy of 87% on a benchmark of 50 structured research tasks

TECHNICAL INDEX

PythonClaude APILangGraphTavilyPydantic+ 1
Access File→

BLOG

ARTICLES & ESSAYS
All Posts →§ SECTION II

N° 01 · APR 2026

OPENAI AGENTS SDK, FROM FIRST PRINCIPLES

10 MIN READ · LLMs · Agents

An agent is an LLM with tools and a goal. The SDK gives you a vocabulary — Agent, Runner, Tool, Handoff, Guardrail, Session — for the patterns you would have built yourself anyway.

Start with what an LLM cannot do. It can answer almost any question you put in front of it, but only up to the date its training stopped, and it cannot reach into the world. It cannot click a button, fetch today's stock price, or write a row to your database. It can only talk.

Continue Reading →

SKILLS

DOMAINS OF PRACTICE · 4 DOMAINS · 24 ENTRIES
§ SECTION III

LANGUAGE · CH 01

LLMs & GENAI

  • Prompt Engineering
  • RAG Architecture
  • Fine-tuning
  • LangChain / LangGraph
  • Agentic Systems
  • Evaluation Harness

6 Entries

VISION · CH 02

COMPUTER VISION

  • Object Detection
  • Image Classification
  • Segmentation
  • Model Optimization
  • Data Annotation
  • Edge Deployment

6 Entries

OPERATIONS · CH 03

MLOps & INFRA

  • ML Lifecycle Mgmt
  • CI/CD for ML
  • Data Versioning
  • Experiment Tracking
  • Deployment on Cloud
  • Monitoring & Alerting

6 Entries

ANALYTICS · CH 04

DATA SCIENCE

  • Feature Engineering
  • Statistical Modeling
  • Explanatory Analysis
  • Data Pipelines
  • Time Series
  • Forecasting & A/B

6 Entries

LEGEND —FAMILIARWORKINGSTRONGEXPERT

ABOUT

BACKGROUND & EXPERIENCE
§ SECTION IV

— PROFILE

SUBJECT demonstrates advanced capability in designing and deploying AI systems under production constraints.

Not purely a model builder — SUBJECT operates across the full stack: data, model, system, and interface. Has a documented history of reducing model deployment timelines, improving inference efficiency, and building evaluation frameworks that catch regressions before they reach users.

Prefers uncomfortable questions about data quality over comfortable conversations about model architecture. Got into AI not because of the hype — but because understanding how humans process information is, still, the most interesting engineering problem there is.

EXPERIENCE5 Entries

● DEC 2025 — PRESENT

Student Instructor

University of Ruhuna — Dept. of Electrical & Information Engineering

Supporting lab sessions for Introduction to Electronics and Analog Electronics, guiding students through experiments and coursework.

MAY 2025 — NOV 2025

Trainee AI & Data Science Engineer

Codezela Technologies

Built RAG pipelines and agentic AI systems with FastAPI and Docker, integrating semantic search to improve retrieval accuracy and relevance.

● 2022 — PRESENT

B.Eng (Hons) Computer Engineering

University of Ruhuna

Undergraduate coursework in computer engineering; active member of the university's IEEE student branch.

NOV 2020 — OCT 2023

Teacher

Self-employed

Taught Mathematics and Physics to A/L students and volunteered tutoring O/L students in underserved and remote areas.

2015 — 2019

Secondary Education (O/L & A/L)

St Joseph's College, Maskeliya

Served as Assistant Senior Prefect and secretary of the Maths & Science Forum.

CONTACT

TELEGRAPH DESK · ● AVAILABLE FOR WORK
§ SECTION V

— THE TELEGRAPH DESK

Open to full-time roles, contract engagements, and interesting problems. If you're building something that requires an engineer who thinks about the whole system — let's talk.

[ SEND WIRE ] REPLIES < 48H

— Jegan.T, AI Engineer

— OTHER LINES

Direct dispatches preferred · Cold email welcome