Senior Full-Stack Engineer (Agentic AI + FinTech) – Stock Adviser Platform

@AwaisChaudhry

Hello, I’m Muhammad Awais, a senior full-stack engineer with hands-on experience in Python backends, event-driven systems, and fintech data workflows. For your Senior Full-Stack Engineer project, I will deliver a robust backend (FastAPI or Node/Nest) hosted on Replit or similar, with an agent/orchestration layer to ingest data, run analytics, generate deterministic recommendations, and monitor health. I’ll design a multi-tenant data model with versioned audit logs and reproducible outputs. External APIs for ASX prices, fundamentals, and news/sentiment will be pluggable, with clean REST APIs for adviser portals. Background jobs via Celery or BullMQ will handle ingestion, retries, and idempotency. I’ll implement model portfolios, risk profiles, and tenure-based allocation (1–5 years) with auditable decisions and explainability. The initial release will be MVP-safe with room for PDF reporting and compliance features later. 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? If an adviser generates a recommendation today and we need to reproduce it exactly 18 months later, what must be stored and versioned? How would 1-year vs 5-year horizon affect portfolio construction and risk constraints? How would you design the system so that switching market data providers does not impact business logic or calculations? What ar

@MashoodurRehman1

I am a Senior Full-Stack Engineer with extensive experience in building data-driven, FinTech platforms using FastAPI and Node.js. My expertise in developing multi-tenant systems and deterministic logic strongly aligns with your portfolio intelligence platform for Australian financial advisers. With a background in building agentic systems, I have successfully integrated complex APIs and backend services to ensure auditability and reproducibility in financial systems. Having worked with Postgres for data management and Celery for background jobs, I can design and implement an agent/orchestration layer that ingests and analyzes market data, handling idempotency and retries effectively. My familiarity with financial systems enables me to develop robust risk profiling and tenure-based allocation logic, ensuring compliance and detailed audit logging. I prioritize clean REST APIs to facilitate seamless UI integration. I am interested in discussing your specific needs and expectations further. Could you provide more insights into the desired scalability features? I look forward to exploring how I can contribute to your platform’s success.

@neerajdhameliya

Hello, Screening Questions: 1. Layer design: Scheduled orchestration with isolated tasks, unique run IDs, idempotent writes, and retries with backoff and checkpoints. 2. Reproducing recommendations 18 months later: Requires storing and versioning raw market data snapshots, model logic/version, parameters, constraints, configuration, and exact code commit or container hash. 3. 1-year vs 5-year horizons: 1-year prioritizes low volatility, tighter drawdowns, and liquidity. 5-year allows higher risk, more equity exposure, and smoother constraints. 4. Switching market data providers: A data abstraction layer normalizes all market data; business logic consumes internal models, so only adapters change. 5. Common mistakes without financial experience: Ignoring survivorship bias, unrealistic assumptions, mixing nominal and real returns, underestimating transaction costs, and building backtests that cannot survive live conditions. 6. Phase 1 exclusions: Skip complex scenario simulations, real-time streaming, multi-region redundancy, and advanced optimization. Phase 1 should prove correctness, determinism, and core value. I’m excited to build the B2B portfolio intelligence platform for Australian financial advisers. Preferred Stack: Python with FastAPI, Celery, Pydantic, and SQLAlchemy/PostgreSQL for deterministic, auditable, and scalable systems. I can start 40 hours per week. I’m available for a quick call to discuss details! Best, Niral

@ashikmohann

Hi, I’m an experienced AI/ML Engineer with 6+ years of hands-on experience building production-grade machine learning and LLM-based systems. I’ve delivered end-to-end solutions including data pipelines, model training, deployment, monitoring, and scalable APIs using Python, FastAPI, Docker, and cloud platforms (AWS/Azure/GCP). I focus on clean, reliable implementations with strong attention to performance and real-world usability. I’d be happy to discuss your requirements and propose a clear, efficient approach to deliver results quickly. Looking forward to working with you.

@LiveExperts

Hello, As an established team of professional engineers and developers at Live Experts, specializing in backend development, cloud computing, and full-stack development, we are perfectly in sync with your project requirements. With extensive experience in building both agentic and data-intensive systems, we are well-versed in the core aspects underpinning your stock adviser platform - portfolio analytics, risk profiling, API integrations, and more. Our expertise is largely centered around Python and FastAPI, ensuring your platform benefits from streamlined code and efficient implementation. We also have a strong background in financial systems which include logic-based algorithms, risk management and familiarity with market data providers – ASX included. This duality allows us to appreciate the nuances of deterministic logic while adhering to strict auditability. In terms of agent/orchestration layer design, we ensure daily ingestion of market data with efficient analytics and precise portfolio recommendations. We prioritize idempotency through robust retry mechanisms to justify and track all decisions. When thinking about producing exact 18-month-old recommendations, we understand that storing all relevant data including market conditions is crucial as business needs may evolve. Our team also comprehends the different implications a 1-year or 5-year investment horizon may have on portfolio construction and risk management - vital Thanks!

@nikhilgarg96

As an experienced full-stack engineer with extensive background in both backend and frontend technologies, I am well-equipped to tackle your project. In terms of relevant systems, I have developed agentic and FinTech-focused applications in both Python and Node.js, giving me a broad perspective to bring to this task. My preference lies with Python (FastAPI) as I find it perfectly suited for deterministic logic, auditability, and versioning – traits key for the success of this platform. Designing an agent/orchestrative layer entails ensuring timely ingestion of market data and generating accurate portfolio analytics. I would achieve this by implementing robust queues, ensuring the data is processed by workers, and including cron jobs where needed. For idempotency and retries, I would create mechanisms that log any failures, including steps for redelivery without creating duplicates. My approach to versioned data storage enables exact reproduction of recommendations even after substantial time lapses. Thanks....

@Microlent

Hello! I read the brief carefully. This is not a consumer trading app — it’s an adviser-grade, deterministic portfolio intelligence system where auditability and reproducibility matter more than AI novelty. Preferred stack: Python + FastAPI + PostgreSQL with background workers. Python fits portfolio math, analytics, and explicit orchestration better than opaque agent loops. 1. Agent/orchestration design: I’d use a step-based, idempotent pipeline: ingestion → analytics → recommendation → monitoring. Each run has a run_id; inputs and outputs are immutable and keyed by org, model, date, and version. Retries are safe because every step is deterministic and replayable. 2. Reproducing results after 18 months: Store/version market data snapshots, model portfolios, risk parameters, horizon rules, calculation logic version, config flags, and final outputs. Reproducibility requires freezing data + logic, not just results. 3. 1y vs 5y horizon: Affects risk budget, volatility limits, asset mix, and rebalancing. Short horizons constrain downside tightly; long horizons allow higher variance and mean reversion. These live as explicit constraints, not heuristics. 4. Switching data providers: Provider adapter layer with normalized internal schema. Business logic never depends on provider-specific formats. 5. Common mistakes: No versioning, mixing predictive AI with deterministic rules, ignoring audit trails, treating live data as truth. Happy to discuss next steps. Best, Jenifer

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Cloud Computing, PostgreSQL, Software Development, Risk Management, Full Stack Development, Backend Development, Data Integration, Database Management, API Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@rahuliitm69

Hi there, I can build your adviser-grade portfolio platform and I have real-time experience working with FinTech systems, doing portfolio logic, risk profiles, multi-tenant setups, and market data integrations. For the orchestration, I will set up a simple event-driven system that pulls daily market data, calculates portfolios, and produces reproducible recommendations. I will make sure the backend, APIs, and portfolio logic are solid, and that market data providers can be switched without breaking anything. For other questions, it’s hard to answer here because of the 1500-word limit. We would request to connect once over chat where i will answer remaining questions and go through your stack, APIs, and workflows. Thanks, Rahul A.

@Julian29

Hello, I have 11+ years of proven experience building multi-tenant, data-heavy FinTech platforms and confidently understand the need for deterministic, auditable portfolio intelligence for advisers. The goal is to deliver a production-grade, explainable portfolio recommendation engine with strong audit/versioning and pluggable market data integrations. -->> Agentic orchestration layer (ingestion → analytics → recommendation) -->> Deterministic model portfolios + risk profiling + tenure-based allocation -->> Multi-tenant data model with audit logs + versioned recommendations -->> Pluggable external API integrations (ASX prices, fundamentals, news) My preferred stack is Python + FastAPI + PostgreSQL + Celery, because it’s ideal for data pipelines, reproducibility, and deterministic workflows while staying highly maintainable. Let’s continue in chat as I have some queries to ask regarding the project to proceed further. Thanks Julian

@technosystem

As a seasoned full-stack engineer with a strong focus on backend development, I am confident in my ability to tackle the challenges your stock adviser platform presents. I have an extensive background in using high-level programming languages like Python with FastAPI, and building event-driven systems complete with cron and worker configurations. The fact that I've built systems similar to yours, such as agentic and FinTech programs with data-heavy requirements, is a clear differentiator. Aside from having an affinity for financial systems, I am particularly drawn to your emphasis on deterministic logic and reproducibility. In designing the agent/orchestration layer, my approach would involve daily data ingestion into a queue system that delays processing until data is fully available. Additionally, incorporating idempotency checks and exhaustive logging minimizes the risk of failure or data loss during retries. Versioning relevant data (including market conditions) regularly ensures exact reproduction of recommended portfolio constructions when required. One of the most important skills I bring to the table is shielding business logic from vendor switching impacts. My architecture focuses on abstraction layers that isolate external API integrations in a non-invasive way, allowing easier replacement when necessary without compromising logic or calculations.

@OutsourceMan

⭐⭐⭐⭐⭐ Valuable Client, CnELIndia and I, Raman Ladhani, can deliver this adviser-grade platform with a deterministic, auditable backend built in FastAPI, PostgreSQL, and a modular data-provider layer. We propose: (1) a task-driven orchestration engine using queued jobs for ingestion, analytics, and recommendation generation; retries use exponential backoff with idempotency keys to ensure exactly-once effects. (2) To reproduce recommendations months later, we store versioned models, input datasets, risk settings, provider snapshots, and allocation rules. (3) Tenure affects risk bands: 1-year horizons bias toward lower-volatility assets and tighter drawdown limits; 5-year allows higher equity exposure and longer rebalancing cycles. (4) Data-provider switching is handled via an adapter interface so business logic consumes normalized structures only. (5) Common mistakes include ignoring volatility regimes, survivorship bias, and failing to version inputs. (6) Phase 1 excludes polished UI, PDF reports, and advanced compliance workflows so we focus on core data, logic, and auditability.

@maximg10

⭐Hi, I’m ready to assist you right away!⭐ I believe I’d be a great fit for your project since I have extensive experience in building complex financial systems and event-driven architectures using Python and FastAPI. With a strong background in developing agentic systems and working with financial data, I am well-equipped to design and implement the agent/orchestration layer along with integrating external APIs to provide accurate market insights to Australian financial advisers. This project aims to create a multi-tenant portfolio intelligence platform that addresses the specific needs of financial advisers by offering model portfolios, risk profiling, and allocation logic based on different tenures. The system will enhance adviser decision-making with deterministic recommendations and full audit logs for compliance. If you have any questions, would like to discuss the project in more detail, or would like to know how I can help, we can schedule a meeting. Thank you. Maxim

@techplusintl

Hi there, ★★★ Python / API Development Expert ★★★ 5+ Years of Experience ★★★ To complete this project, I will focus on building a robust backend system tailored for financial advisers. The key steps include: 1. Analyze project requirements and finalize technology stack (Python FastAPI or Node.js) Estimated hours: 4 2. Design and implement the agent/orchestration layer to handle data ingestion and analytics Estimated hours: 10 3. Integrate external APIs for ASX prices and other market data sources Estimated hours: 8 4. Develop model portfolios and risk profiling logic Estimated hours: 10 5. Design a multi-tenant data model with audit logs and version control Estimated hours: 6 6. Expose REST APIs for the adviser portal Estimated hours: 6 What I need from you: 1. Access to any existing documentation or specifications for the platform 2. Information on preferred external market data providers 3. Feedback on initial designs and architecture to ensure alignment with your vision. I look forward to connecting at your convenience to ensure the project's success. Best Regards, TechPlus Team

@softwarelinkerrs

With vast experience in backend development using Python, API Development, and Full Stack Development, I am confident in my ability to build and maintain your financial intelligence platform. In my previous project, I designed an agentic system that effectively ingested data, generated analytics, offered deterministic recommendations, and minimized failures through robust retry mechanisms. This system had the provision to store and version portfolio recommendations down to the very detail should reproducibility be necessary - exactly what your project demands. Understanding the complexities of a financial system is critical for its success, and I'm thankful for the robust background I have in portfolio logic, market data integration, risk management which includes tenure-based(1-5years) allocations and multi-tenancy strategies which ensures optimal utilization of resources while maintaining security. Anticipating the need for scalable computations with large datasets, my proficiency in SQL(PostgreSQL) and background jobs (Celery/BullMQ) will definitely streamline your system for enhanced performance and audit logs. As an engineer with strong domain experience in finance who has built similar products for ASX-focused advisers before, I am aware of the potential pitfalls – such as miscalculations due to erroneous data or suboptimal risk constraints. My aim has always been continuous improvements on existing systems and solutions tailored to your business goal.

@SBITServices

Hi Gaurav, I have extensive experience in building agentic systems and financial platforms, making me well-equipped for your Senior Full-Stack Engineer project. I propose to design a robust backend system using FastAPI, integrate external APIs, implement model portfolios, and prioritize deterministic logic and auditability. Let's discuss this further in detail. Regards, Sai Bhaskar

@shanec35

Hello, I’ve carefully reviewed your requirements and recently built a multi-tenant financial analytics backend for advisers, including model portfolios, risk bands, time-horizon allocation, market data ingestion, and full audit/versioning to support compliance and reproducibility. The system used deterministic rules, background workers, and pluggable data providers. Your platform’s key needs—agent orchestration, ASX-focused data ingestion, tenure-based allocation, explainable recommendations, and strict audit logging—will be addressed with a clear orchestration layer (ingestion → analytics → recommendation → monitoring), versioned portfolio logic, immutable inputs/outputs, and provider-agnostic adapters so data sources can be swapped without touching core logic. I’m available to start immediately and committed to delivering a high-quality, adviser-grade system as efficiently as possible, with clean APIs and a scalable foundation for future phases. Best regards, Elenilson

@topcreator100

Hi there, I’ve read your Senior Full-Stack Engineer brief for an adviser-grade, ASX-centric, multi-tenant Stock Adviser Platform. I’m confident I can deliver a robust backend with deterministic logic, explainability, and full audit/versioning. With 6+ years building FinTech backends in Python (FastAPI) and Node, I’ve designed agent-like orchestration systems, event-driven data flows, and scalable PostgreSQL schemas. My approach is to build an explicit agent/orchestration layer that ingests daily market data via pluggable providers, runs idempotent analytics, and outputs reproducible, auditable recommendations. I’ll implement model portfolios, risk profiles, and tenure-based allocation, plus full audit logs and versioned recommendations. The system will expose clean REST APIs for the adviser portal and support background jobs (Celery or BullMQ) to keep data fresh without impacting front-end latency. I can start with a FastAPI backend (or Nest/Node if you prefer) and deliver an MVP in 2–3 weeks with iterative improvements. Next steps: share your preferred data providers and any regulatory/compliance formats, and I’ll draft a concrete sprint plan and milestones. Best regards,

@abhisaini0188

Hello, Thank you for sharing this opportunity, it sounds like a great fit, and I’d be glad to be involved. I’ve worked on similar projects and am confident I can contribute meaningful value to your team. I focus on delivering high-quality, reliable solutions while ensuring the process is smooth and efficient for my clients. My goal is always to build solutions that are both technically solid and easy to maintain. You’re welcome to review my profile to see examples of my previous work. If it aligns with what you’re looking for, I’d be happy to discuss your project in more detail and outline how I can support your goals. I’m available to start immediately and can dedicate my full attention from day one. Let’s connect and explore how we can make this project a success together. Looking forward to your response. With Regards! Abhishek Saini