Senior Quant / AI Trading Engineer – Multi‑LLM SPX & ES 0DTE/1DTE BotYenom Capital • Belgaum, Karnataka, India

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

Yenom Capital • Belgaum, Karnataka, India

6 days ago

Job description

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 : / / 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)
A 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 skillsComfortable 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.

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Senior Ai Engineer • Belgaum, Karnataka, India

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