00.001
Senior Technical Product Manager

Product Leader Who Ships Production Systems.

⚡30 → 6 min review
🛡️5-stage risk gate
🧠24 strategy templates
🏆 13+ Years Experience
📍 Chennai, India
Open to new opportunities
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Engineering Track RecordFull history on LinkedIn
13+
Years in Production

FinTech & enterprise systems

32
Systems Shipped

Trading, AI & enterprise

5
Pre-Trade Checks

Between every signal and the exchange

2
Brokers, One Order API

Zerodha and Flattrade behind one interface

3
AI Agents in Parallel

Merged into one de-conflicted report

30→6 min
Review Cycle Cut

PII-safe AI middleware

Architecture and throughput figures — each one demonstrable in a live walkthrough.

WHAT I DELIVER

Core Expertise

Product Management + Engineering Execution + Operational Excellence.

Product Strategy & Execution

Led 6+ zero-to-one product launches across enterprise and FinTech. Specialize in turning ambiguous requirements into shipped products with measurable business impact.

DELIVERABLES
PRDs & SpecsUser ResearchGTM StrategyRoadmapping

AI & Systems Architecture

Build production AI systems designed to clear enterprise security and data-handling review. PII-safe middleware that turns a 30-minute manual process into a 6-minute automated one. Real systems, not demos.

DELIVERABLES
RAG SystemsAgentic WorkflowsVector DBsSystem Design

Technical Execution & Prototyping

Full-stack builder who prototypes to validate concepts. Two strategy engines dispatching against six configured entries, over a single order API spanning two brokers — with every refusal logged rather than silently dropped.

DELIVERABLES
Full StackPython/FastAPIReact/Next.jsRapid Prototyping

Operational Excellence & Risk

Designed automated risk gates that reject non-compliant orders before they reach a broker — kill switches, pre-trade validation, and audit trails. Built systems that survive real-world chaos.

DELIVERABLES
Risk GatesProcess AutomationIncident MgmtScalability
System Active • 2024

STRATUM ALPHA

Institutional-grade quantitative validation engine.
My Role: Product Lead & Technical Architect
Python • NumPy • Joblib • Plotly

01
CASE STUDY
/// THE CHALLENGE

Trading strategies break.
Most fail before they start.

⚠️ Overfitting is the Enemy

Most traders find a strategy that worked last week and bet money on it. This is "Curve Fitting"—mistaking luck for skill. Business Risk: Strategies that fail in live markets destroy capital and credibility. I needed a system that could rigorously stress-test strategies across 5 years of market data.

TRADITIONAL
LUCK
🐢

Standard Tools Fail

TradingView & Excel cannot run
a parallel parameter sweep.

/// THE SOLUTION

Brute-Force
Engineering.

We don't guess parameters. We scan the entire mathematical universe of possibilities to find stable regions of profitability.

01

Vectorization Engine

Replaced slow Python loops with NumPy vector operations. Utilized CPU SIMD instructions to process entire market datasets in single clock cycles.

⚡ 500x SPEEDUP
02

Parallel Core Forking

Optimization is an "Embarrassingly Parallel" problem. I used Joblib to fork the validation process across all 16 CPU threads, saturating hardware limits.

🔥 100% CPU LOAD
03

3D Robustness Mapping

Instead of a single "best number", we generate 3D heatmaps to find "Plateaus of Stability"—regions where profits survive even if market conditions drift.

🗺️ VISUAL PROOF

OPTIMIZATION_ENGINE.PY

OPTIMAL CONFIG

YAML-defined grid → 1 optimalVALIDATED
500x
Compute Speed
16
Cores Maxed
10k+
Sims / Batch
3
Crises Averted

REFLECTION

"This validation engine prevented deployment of 3 strategies that 'looked profitable' but failed rigorous stress-testing. Saved potential losses while proving that Validation > Optimization.

Business Impact: Enabled institutional-grade confidence in strategy deployment—critical when real capital is at stake."

Deployed • v1.6

AI STRATEGY COPILOT

Zero-to-One FinTech UX for Options Execution.
My Role: Product Lead, Full-Stack Builder & AI Integration
Google Gemini • FastAPI • Zerodha API • Risk Engine

02
CASE STUDY
/// THE CHALLENGE

Finance isn't creative writing.
Ambiguity is expensive.

⚡ The Intent-Execution Gap

Junior traders often know what they want ("I'm bullish, but keep it safe") but fail at the how. Constructing a multi-leg options strategy requires finding 4 specific strike prices, calculating Deltas, and placing 4 separate orders in seconds. Manual execution is slow and prone to "fat finger" errors.

MANUAL: 4 MINS
ERROR PRONE
🧠

LLMs Can't Do Math

Use ChatGPT for English.
Use Python for Math.
Never mix them.

/// THE SOLUTION

Hybrid
Reasoning Engine.

We built a split-brain architecture. Google Gemini handles the vague human language, while a deterministic Python engine handles the precise financial mathematics.

01

Intent Recognition

Google Gemini acts as the translator. It takes vague commands like "Safe bullish bet" and extracts structured JSON tags: {"view": "BULLISH", "risk": "LOW"}.

🗣️ NATURAL LANGUAGE
02

Risk-Adjusted Engine

The Python engine doesn't just execute; it profiles. It filters 50+ possible strategies against the user's risk tier (Low/Mid/High).

Simulates max-loss scenarios to ensure the strategy matches the 1:3 Risk/Reward requirement.

🛡️ DYNAMIC HEDGING
03

Unified Execution

Once the optimal strategy is found, the engine fires API calls to Zerodha Kite Connect. It executes complex multi-leg orders across 10+ connected accounts simultaneously.

🚀 MULTI-ACCOUNT
COPILOT_INTERFACE.V1
"I want a safe bullish bet on Nifty."
GEMINI INTENT PARSER
{
  "view": "BULLISH",
  "risk_profile": "LOW",
  "hedging": "MANDATORY"
}
RISK ENGINE SCANNER
NAKED CALL (HIGH RISK)REJECTED
BULL PUT SPREAD (MED RISK)RR < 1:2
IRON CONDOR (LOW RISK)MATCHED
STRATEGY OPTIMIZER
NIFTY 24000 CEBUY (HEDGE)
NIFTY 24200 CESELL (PREMIUM)
Max Loss: Capped ₹2kProb: 72%
<10s
Execution Time
0%
Math Errors
10+
Accounts Linked
v1.6
Live System
Flagship Systems

Built to run unattended

Six systems that hold state, recover from failure, and keep working when nobody is watching. The panel tracks whichever one you're reading.

Trading Systems01 / 06

Enterprise Algo Execution Platform

Multi-broker execution engine with a unified control surface

A production trading platform that routes orders across two brokers from one Next.js control surface. Every signal clears a five-check risk gate before it can reach an exchange — kill switch, circuit breaker, liquidity, capital adequacy, daily loss — and each refusal is logged with the reason, so a trade that never happened can still be explained.

5
Pre-trade checks
2
Broker adapters
2 live · 6 configured
Strategy engines
  • Single unified order API over Zerodha and Flattrade
  • Five-stage risk gate between every signal and the exchange
  • Every refusal logged with its reason code, not silently dropped
PythonAsyncIONext.jsWebSocket
Enterprise AI02 / 06

Ethereal Website Audit Engine

Three AI agents audit a site in parallel and merge into one report

Point it at any URL and three specialised agents review it simultaneously — one for UX, one for marketing, one for business model. A compressed snapshot layer distils the rendered page before it reaches the model, and the compression ratio it achieves is logged on every run.

3
Parallel agents
3 concurrent, capped
Model calls
Rotates on quota
Key pool
  • Three specialised agents (UX, marketing, business) running in parallel
  • Structured snapshot replaces the raw page dump before any model call
  • Playwright renders JavaScript-heavy pages before analysis
PythonPlaywrightGemini 2.5 FlashStreamlit
Enterprise AI03 / 06

AI Options Strategy Copilot

Plain-English intent in, precise multi-leg structure out

Type "I'm bullish but want downside protection" and it selects the matching options structure, computes the strikes, and stages execution across every linked account, unwinding any partly-filled basket. The split matters: an LLM classifies intent only, while every strike, spread width, and lot size is computed by deterministic Python — so the model can never invent a number that reaches your broker.

24
Strategy templates
Deterministic
Strike calculation
2s (15s off-hours)
Live P&L push
  • Natural-language intent classification mapped to defined strategy templates
  • All strike and payoff maths computed deterministically, never model-generated
  • Partial-fill unwind — a basket that cannot complete is reversed leg by leg
PythonGoogle GeminiZerodha KiteConnectWebSocket
Trading Systems04 / 06

RSI Scalping React Platform

Full-stack scalping system with autonomous exit management

Evaluates RSI two-candle body reversals at each one-minute candle close and manages the whole trade lifecycle without intervention. Once filled, the entry is split into three separately-tracked target legs at 1.5R, 2.0R and 2.5R, and open legs are rehydrated from the trade log on restart so a crash never orphans a position.

60s close · 5s LTP
Scan cadence
3, independent
Profit tiers
Autonomous
Exit handling
  • RSI(4) two-candle body reversal evaluated at each 1-minute candle close
  • Each entry split into three target legs (1.5R / 2.0R / 2.5R), tracked separately
  • Autonomous exit management — no manual intervention after entry
PythonFastAPINext.jsTypeScript
Trading Systems05 / 06

GSheets Supertrend Signal Bot

Automated Supertrend execution with a human-readable audit trail

A Supertrend options bot built around an explicit state machine, so the system is always in exactly one known state and cannot double-enter or orphan a position on restart. Every transition writes to Google Sheets and CSV, giving a complete trade record you can read without a database client.

State machine
Execution model
CSV + JSON status
Audit trail
Exclusive lock
Concurrency guard
  • Explicit state machine preventing double entries and orphaned positions
  • Every lifecycle event written to a CSV audit trail with source and severity
  • Automatic signal capture — no missed alerts from manual monitoring
PythonGoogle Sheets APIZerodha KiteConnectState Machine
Enterprise AI06 / 06

ServiceNow Change Validator

AI middleware that reviews Change Requests before a human does

Sits between ServiceNow and your change approval board, automatically assessing every Change Request for compliance gaps, risk level, and missing detail. A PII scrubbing layer strips identifying data before anything reaches the model, so the integration stays within enterprise data-handling policy.

30 min → ~6 min
Review time
PII-scrubbed
Data handling
Native ServiceNow API
Integration
  • Automated compliance, risk, and completeness assessment per request
  • PII scrubbing layer applied before any model call
  • Cuts a ~30-minute manual review to roughly 6 minutes
PythonGoogle GeminiServiceNow APIFlask
View all 32 systems

The Archive

Enterprise AI

Enterprise PlanGuard

PII-sanitized AI middleware that coaches engineers on Change Requests, turning a 30-minute manual review into roughly 6 minutes.

Python • LangChain • ServiceNowVIEW →
High Frequency

Algo Execution Platform

Centralized orchestration engine routing two strategy engines across two brokers, with a five-stage risk gate between every signal and the exchange.

Next.js • AsyncIO • RedisVIEW →
FinTech Ops

Finance Command Center

Local-first personal finance system with automated net-worth tracking and push-notification billing alerts.

FastAPI • Streamlit • TelegramVIEW →
Algo Trading

Agentic Grid Bot

Resilient trading bot treating Google Sheets as a "Source of Truth" database for collaborative state management.

Python • Google Sheets APIVIEW →
Integration

Signal Bridge

Middleware connector bridging TradingView Webhook alerts to MT5 desktop execution with auto-risk calculation.

Flask • Ngrok • MetaTrader 5VIEW →
Internal Tools

Team Productivity OS

Zero-cost resource management dashboard with strict RBAC, replacing complex Jira workflows for agencies.

Next.js • Google Sheets BackendVIEW →
How These Systems Are Built

The decisions that hold up in production

Anyone can demo a working script. These are the choices that decide whether it still works on day ninety, unattended, with real money moving through it.

Failure is designed for, not patched

Kill switches, pre-trade validation, and automatic unwind logic are built in from the start — not bolted on after the first incident.

No black boxes

Every system keeps a readable audit trail — Google Sheets, CSV, or SQL — so you can always answer why it did what it did.

AI where it belongs, maths where it matters

Models classify intent and summarise. Numbers that reach an order are computed by deterministic code, so a model can never invent one.

Restartable by default

State lives in Postgres, SQLite, or Sheets — never only in memory. A crash mid-session resumes cleanly instead of orphaning positions.

Latency is a feature

Async event loops, vectorised computation, and batched API writes — because in execution systems, being right slowly is still being wrong.

You own what you buy

Full source, no obfuscation, no licence server that can switch off. Documentation written for the person who inherits it, not just the person who built it.

THE PRODUCT LEADER

Product Leader Who
Ships Systems That Scale

13+ years owning product across FinTech, trading, risk and enterprise AI, on platforms that cannot go down during market hours.

  • ✔
    13+ Years Owning ProductFinTech, Trading & Risk Platforms, Enterprise AI
  • 🎯
    Measurable Impact
    30 → 6 min review5-stage risk gate2 brokers, 1 API
  • 🚀
    Strategy Through DeliveryDiscovery & PRDs • Prioritisation • Production

Specialize in building 0-to-1 products where ambiguity meets high technical complexity. Experience shipping AI systems built to enterprise security standards, trading platforms that handle real money, and automation that actually works.

"Great systems aren't flashy. They're reliable, scalable, and quietly solve hard problems."

Suresh Balaraman, Technical Product Manager
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LET'S COLLABORATE

Let's Build Something
That Actually Works

Always happy to talk about systems, architecture, and product — get in touch.

If you're hiring for a Senior Technical Product Manager role — someone who can write the spec and then build the thing — I'd be glad to talk.

Notice
90 days or earlier
Based
Chennai, or remote
Relocation
Open to relocating, including outside India
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