Lead Algorithmic Trading Developer

Algorithmic Trading Developer
(Execution, Risk, ML & Infrastructure)
Level: Mid-level (3–5 years hands-on algorithmic/quantitative trading experience)
Engagement: Full-time, in-house
Markets Covered: Forex (MT4/MT5) • Crypto exchange APIs • Equities and F&O broker APIs
Reports To: Head of Algo Trading / Founder
About AJAlgo
AJALGO runs a portfolio of automated trading bots across Forex (MT4/MT5), crypto exchanges, and equity/F&O broker APIs. We build and operate systems that don't just generate trading signals, they execute them reliably, enforce risk discipline in code, and stay resilient around the clock. Our focus is on building robust, well-monitored infrastructure where every trade is accounted for and every risk limit is enforced automatically, not manually. We're a small, hands-on team where engineering rigor and calibrated scepticism matter as much as trading performance.
About the Role
We run a portfolio of automated trading bots across Forex (MT4/MT5), crypto exchanges, and equity/F&O broker APIs. The bots generate the signals; this role owns everything that happens after the signal, and everything the bots depend on to stay alive.
What You'll Own
Signal Execution & Monitoring
Own the order pipeline end to end: signal, risk checks, order routed, fill confirmed, position tracked, logged
Build and maintain a live monitoring dashboard (positions, P&L, exposure, latency, bot heartbeat)
Handle rejected orders, partial fills, requotes, slippage, disconnects, and stale prices explicitly
Run daily reconciliation between bot-held positions and broker/exchange records
Maintain a real-time alerting system with clear severity levels
Money Management & Risk Control
Implement position sizing in code: fixed-fractional risk, volatility-adjusted lots, hard caps per symbol/strategy/account
Enforce daily/weekly loss limits, drawdown stops, and margin safeguards
Track correlated exposure across bots and instruments
Guarantee every position carries a stop loss, including after restarts
Produce daily risk reports
Machine Learning on Trade History
Build clean, versioned datasets from historical trades and market context
Engineer features while avoiding look-ahead bias
Train models for signal filtering and position sizing (meta-labelling approach)
Validate using walk-forward and purged/embargoed cross-validation
Deploy with shadow, small allocation, then full allocation, with rollback plans
Monitor for model drift and define retraining triggers
Server & Trading Terminal Operations
Provision and maintain Linux VPS (bots) and Windows VPS (MT4/MT5 terminals) near broker/exchange servers
Run services under process supervision for automatic recovery
Set up uptime, resource, and heartbeat monitoring with alerts; maintain exact NTP sync
Harden access: key-based SSH, vaulted secrets, least-privilege API keys
Maintain and test backups of trade database, config, and model artefacts
Document failover and disaster-recovery runbooks
Must-Have Skills & Experience
3–5 years building/operating automated trading systems that traded real money (not just backtests)
Strong Python (pandas, NumPy, asyncio, REST/WebSocket clients); Git, structured logging, tests for money-touching code
Working knowledge of MQL4/MQL5 or driving MT5 from Python; understands magic numbers, order lifecycle, hedging vs. netting
Experience integrating at least one crypto exchange API and one broker API; rate limits, idempotent order IDs, reconciliation
Fluent in market mechanics: spread, slippage, swap/funding, margin, leverage, partial fills, rollover
Can compute correct lot size from equity, stop distance, tick value, and risk % on the spot
Practical ML experience (scikit-learn, XGBoost/LightGBM); understands overfitting, leakage, and why standard k-fold fails for time-series
Confident on Linux (SSH, systemd, cron); comfortable administering Windows VPS for MT terminals
SQL competence with trade/time-series data (PostgreSQL/TimescaleDB/SQLite)
Conservative temperament: defaults to halting and escalating rather than improvising with live capital
Good to Have
Experience at a prop firm, hedge fund, HFT desk, or funded-trader programme
Docker, CI/CD, infrastructure-as-code (Ansible/Terraform), Prometheus + Grafana
C++/C# for latency-sensitive components; FIX protocol
Familiarity with triple-barrier labelling, meta-labelling, purged K-fold with embargo
Options/F&O knowledge: Greeks, SPAN margin, expiry-day behaviour
Public repository, research write-up, or verifiable track record
Share Your Resume
at: admin@ajems.com
.

Work arrangement
No