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) Robust 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: [email protected] .

📌 Lead Algorithmic Trading Developer (India)
🏢 AJEMS
📍 India