Senior Full-Stack Engineer (Agentic AI + FinTech) – Stock Adviser Platform
Budget: $25 – $50 AUD
We are building a B2B, multi-tenant portfolio intelligence platform for Australian financial advisers (ASX-focused). The system includes an agent/orchestration layer, model portfolios, risk profiling, tenure-based allocation (1–5 years), external market/news/fundamental APIs, and full audit/compliance logging.
This is not a consumer trading app. It is an adviser-grade system with deterministic logic, explainability, and reproducibility.
Scope of Work (Initial Phase)
Build a backend system (FastAPI or Node/Nest) hosted on Replit or similar
Implement agent/orchestration layer (ingestion, analytics, recommendation, monitoring)
Integrate external APIs (ASX prices, fundamentals, news/sentiment – pluggable providers)
Implement model portfolios, risk profiles, tenure-based allocation logic
Design multi-tenant data model, audit logs, and versioned recommendations
Expose clean REST APIs for adviser portal (UI can be basic initially)
Required Skills
Strong experience with Python (FastAPI) or Node.js (Nest/Express)
Experience building agentic or event-driven systems (queues, workers, cron)
Financial systems experience (portfolio logic, risk, market data) strongly preferred
PostgreSQL, background jobs (Celery/BullMQ), API integrations
Emphasis on deterministic logic, auditability, and versioning
Nice to Have
FinTech / Wealth / Adviser platforms experience
Knowledge of ASX data providers
Experience with PDF reporting, compliance workflows
Frontend experience (React/Next.js or AI UI builders)
Engagement
Contract (20–30 hrs/week to start)
Long-term potential if initial phase goes well
Clear specs provided; founder is product- and tech-literate
To Apply (Important)
Please include:
1–2 relevant systems you’ve built (agentic, FinTech, data-heavy)
Your preferred stack (Python or Node) and why
Briefly explain how you would design an agent/orchestration layer
Generic applications will be ignored. Please answer the following screening questions to qualify.
1. Describe how you would design an agent/orchestration layer that ingests market data daily, generates portfolio analytics, and produces deterministic recommendations. How do you handle retries and idempotency?
2. If an adviser generates a recommendation today and we need to reproduce it exactly 18 months later, what must be stored and versioned?
3. How would a 1-year vs 5-year investment horizon affect portfolio construction and risk constraints?
4. How would you design the system so that switching market data providers does not impact business logic or calculations?
5. What are common mistakes engineers make when building portfolio or risk systems without financial domain experience?
6. Given limited time and budget, what would you deliberately NOT build in Phase 1, and why?
This is not a consumer trading app. It is an adviser-grade system with deterministic logic, explainability, and reproducibility.
Scope of Work (Initial Phase)
Build a backend system (FastAPI or Node/Nest) hosted on Replit or similar
Implement agent/orchestration layer (ingestion, analytics, recommendation, monitoring)
Integrate external APIs (ASX prices, fundamentals, news/sentiment – pluggable providers)
Implement model portfolios, risk profiles, tenure-based allocation logic
Design multi-tenant data model, audit logs, and versioned recommendations
Expose clean REST APIs for adviser portal (UI can be basic initially)
Required Skills
Strong experience with Python (FastAPI) or Node.js (Nest/Express)
Experience building agentic or event-driven systems (queues, workers, cron)
Financial systems experience (portfolio logic, risk, market data) strongly preferred
PostgreSQL, background jobs (Celery/BullMQ), API integrations
Emphasis on deterministic logic, auditability, and versioning
Nice to Have
FinTech / Wealth / Adviser platforms experience
Knowledge of ASX data providers
Experience with PDF reporting, compliance workflows
Frontend experience (React/Next.js or AI UI builders)
Engagement
Contract (20–30 hrs/week to start)
Long-term potential if initial phase goes well
Clear specs provided; founder is product- and tech-literate
To Apply (Important)
Please include:
1–2 relevant systems you’ve built (agentic, FinTech, data-heavy)
Your preferred stack (Python or Node) and why
Briefly explain how you would design an agent/orchestration layer
Generic applications will be ignored. Please answer the following screening questions to qualify.
1. Describe how you would design an agent/orchestration layer that ingests market data daily, generates portfolio analytics, and produces deterministic recommendations. How do you handle retries and idempotency?
2. If an adviser generates a recommendation today and we need to reproduce it exactly 18 months later, what must be stored and versioned?
3. How would a 1-year vs 5-year investment horizon affect portfolio construction and risk constraints?
4. How would you design the system so that switching market data providers does not impact business logic or calculations?
5. What are common mistakes engineers make when building portfolio or risk systems without financial domain experience?
6. Given limited time and budget, what would you deliberately NOT build in Phase 1, and why?