Founding AI Engineer

About TRADL AI

TRADL AI is building India's first AI trading co-pilot for 100M+ traders.

Today, we convert natural language into verified, code-backed stock intelligence - no hallucinations, no black boxes. Tomorrow, we're building the full stack: real-time chart intelligence, autonomous research agents, and portfolio-aware execution. We're turning what institutional desks spend millions on into a conversation anyone can have. We're live, growing fast, and the hardest technical problems are still ahead of us.

Our tech stack spans real-time market data ingestion (Kafka + TimescaleDB processing millions of ticks/day), AI/ML infrastructure (custom LLM orchestration + quant computation engines), a React/Next.js web platform, and native mobile apps - all built for sub-100ms latency because in trading, milliseconds matter.

We are building the engineering team that will scale this from hundreds to hundreds of thousands of concurrent traders.

Why this Role exists

TRADL's core moat is the intelligence layer - the system that takes a natural language trading query, understands intent, decomposes it into quantitative operations, generates executable Python code, and returns mathematically precise results against live market data. Today this works. But it needs to work 100x better: faster inference, richer reasoning chains, multi-step agent workflows, and eventually autonomous execution.

The Founding AI Engineer owns the entire AI/ML stack and is the technical co-architect alongside our CTO.

What You'll Own

LLM Orchestration: Own the pipeline converting natural language → executable quant code. Architect self-hosted models, fine-tuning, and hybrid routing

Hallucination Elimination: Build the verification layer ensuring every AI result is mathematically deterministic. Our deepest IP.

AI-Charts Engine: Real-time AI overlays - automated pattern recognition, predictive annotations, support/resistance. Scale from static to streaming intelligence.

FundAgent: Design the autonomous trading agent: multi-step reasoning, portfolio-aware decisions, risk-bounded execution.

Model Infrastructure: GPU compute strategy, model versioning, A/B testing, latency optimization (

Research → Production Pipeline: Turn academic research into production features. Bridge the Jupyter notebook → live market execution gap.

What We're Looking For

• 5-8 years in ML/AI engineering with at least 2 years working with LLMs in production (actual deployed systems handling real user queries).
• Deep understanding of LLM internals: tokenization, attention mechanisms, prompt engineering, RAG architectures, function calling, and code generation patterns.
• Production experience with model serving: latency optimization, batching strategies, model routing, and cost management across multiple LLM providers.
• Strong Python: You'll be writing the code that generates code. Meta-programming, AST manipulation, and sandboxed execution are daily concerns.
• Familiarity with financial data structures is strongly preferred: OHLCV, option chains, Greeks, order books. You don't need to be a quant, but you need to understand what the generated code is computing.
• Experience building agent/agentic systems: tool use, multi-step planning, state management, error recovery.
  
  

Bonus Points

• Published research or open-source contributions in NLP, code generation, or financial AI.
• Experience with quantitative finance: backtesting frameworks, risk models, signal generation.
• Rust or C++ for performance-critical components.