Senior Quant / AI Trading Engineer – Multi‑LLM SPX & ES 0DTE / 1DTE Bot

Job Title : Senior Quant / AI Trading Engineer – Multi‑LLM SPX & ES 0DTE / 1DTE Bot

Location :

Remote or Onsite (Flexible)

Type :

Full-time / Contract-to-Hire

Compensation :

Excellent base salary + 10% of quarterly trading profits

Ref :

https : / / developer.tastytrade.com / getting-started /

https : / / github.com / virattt / ai-hedge-fund

https : / / github.com / aicheung / 0dte-trader

https : / / github.com / AlexWan / OsEngine

https : / / github.com / marketcalls / openalgo

About Us

We are building a next‑generation

AI-driven options trading system

focused on

SPX and ES 0DTE & 1DTE . Our goal is to systematically capture

5x–10x intraday option moves

by combining : Deep

Technical Analysis (TA)

multi‑LLM “council” architecture

(strategy + critic, similar to llm-council)

Real-time commercial‑grade data

Cross‑asset and macro / event awareness

Automated execution via

TastyTrade APIs

We are looking for a

hands-on Senior Quant / AI Trading Engineer

to design and build this system end‑to‑end.

Role Overview

You will architect and implement a

Smart AI trading bot

that : Trades

ES futures and options overnight / pre‑market

(approx.

6 : 00 PM–9 : 30 AM EST )

Trades

SPX 0DTE and 1DTE options

during regular hours

Uses

multiple LLMs , where :

One LLM

proposes strategies, entries, exits, and risk parameters

One or more LLMs

critique and challenge

those strategies before execution

Incorporates

Technical Analysis, sentiment, volatility, macro events, and cross‑asset flows

Executes

multiple staggered entries and exits

to improve average prices

Analyzes ES from 6 PM EST (prior evening) through 9 : 30 AM EST , trades ES in that window, then

exits or converts ES positions into SPX after ~10 : 00 AM EST

once opening volatility settles

Trades via

TastyTrade APIs , strictly following defined risk parameters

Key Responsibilities

Multi‑LLM Strategy & Critic Engine

Design a

multi‑LLM “council”

where :

A “Strategy LLM” generates trade ideas, entries / exits, size, and risk parameters

“Critic LLMs” stress‑test, challenge assumptions, and flag risks

Implement workflows :

proposal → critique → refinement → final decision , with deterministic risk rules as guardrails.

Technical Analysis & Signal Generation (Must-Have)

Build TA-based signals using :

Multi‑timeframe

trend / momentum indicators

(EMAs / SMAs, VWAP, MACD, RSI, ADX, etc.)

Volatility / range tools

(ATR, gaps, opening range, realized vs. implied vol)

Market structure

(support / resistance, liquidity zones, prior day high / low, overnight levels)

Perform detailed

ES trend analysis

from

6 PM EST (prior evening)

to

9 : 30 AM EST :

Direction, strength, volatility, and key levels

Use that analysis to :

Take

ES trades between 6 PM and 9 : 30 AM EST

Decide whether to

exit or convert ES positions into SPX 0DTE / 1DTE trades after ~10 AM EST .

Trade Management, Scaling & Risk

Implement

multiple entries and exits :

Scaling into positions at predefined technical / volatility levels

Layered profit targets and stop levels to improve average prices

Build a

risk engine

to : Set daily

Max Loss

and

Max Profit

as a % of portfolio

Stop trading once limits are hit

Control max exposure, number of positions, and per‑trade risk

Support

user-selectable : Bias :

Bullish only / Bearish only / Both ways

Profiles :

Conservative / Moderate / Aggressive

(affects size, frequency, and risk per trade).

Macro Events, News & Cross‑Asset Context

Track and integrate

major events , including :

FOMC ,

jobs data / NFP ,

CPI / PPI ,

GDP , etc.

Earnings calendar

(especially large index components)

Important

global geopolitical news

impacting risk sentiment

Use these events to :

Adjust or pause trading around high-risk windows

Feed event context into LLMs for better decision‑making.

Monitor

cross‑asset markets

that drive SPX / ES :

Oil, Copper, Gold, Silver, US Dollar (DXY / FX)

Detect confirmation / divergence patterns between these assets and ES / SPX, and reflect that in :

Trade bias (risk‑on vs risk‑off)

Aggressiveness of entries / exits and position sizing.

Data, Execution & System Design

Integrate with our

commercial-grade real-time data feed

for :

ES, SPX, their options, and key cross‑asset instruments

Build a robust

execution layer using TastyTrade APIs :

Handle order placement, modifications, cancels, fills, and error conditions

Manage slippage, partial fills, and retry logic

Architect a

modular system :

Data ingestion → TA & signals → LLM council → risk → execution → UI / monitoring

Implement

monitoring, logging, and alerting

for :

Strategy decisions & LLM reasoning (traceability)

P&L, risk, exposure, and events

Connectivity and system health

Required Skills & Experience

Must-Haves :

Strong, practical Technical Analysis skills Comfortable with multi‑timeframe chart analysis, indicators, and price action.

4+ years in

quantitative / algo trading or systematic options / futures development

Strong programming in

Python

(or similar, with willingness to build in Python)

Hands-on experience with :

Automated trading systems

using real-time data

Options and / or futures

trading (SPX / ES strongly preferred)

Intraday or short‑dated strategies

Solid understanding of :

Options greeks, IV, skew, and term structure

ES and SPX microstructure, especially around macro events

Risk management and drawdown control

LLM / AI : Experience

using or integrating LLMs

(agents, decision support, tools, etc.)

Familiarity with

multi-agent / council‑style LLM patterns

(proposal vs critic / debate).

Ability to design

prompts, context pipelines, and guardrails

for trading decisions.

APIs & Infrastructure :

Experience with

broker APIs

(TastyTrade is a strong plus; IBKR / Tradier / etc. also helpful)

Familiarity with

real-time data feeds

(WebSocket, FIX, vendor SDKs)

Strong engineering practices : testing, logging, observability, deployment.

Nice-to-Have

Direct experience with

SPX & ES 0DTE / 1DTE

strategies

Experience with :

Cloud (AWS / GCP / Azure),

Docker , and basic DevOps

Dashboards (Streamlit, Dash, Grafana, or custom web UI)

Background in

time-series ML, regime detection, or reinforcement learning

Macro or cross‑asset trading experience.

Compensation

Excellent base salary , commensurate with experience

Attractive % of quarterly trading profits

based on performance

Potential for increased profit share as the system scales.

CTC mentioned is in INR from 25L to 50L + 10% of trading profits paid quarterly.