I'm a software engineer focused on backend engineering, AI systems, and data-intensive platforms. I build systems around LLMs, agentic workflows, trading infrastructure, and operational automation with an emphasis on reliability, observability, reproducibility, validation boundaries, and stakeholder-facing delivery. My background spans AI-enabled customer support automation, RAG/chatbot deployment, LLM and agentic coding evaluation, retail trading platform operations, market data pipelines, ETL, incident response, and backend/API development. I have worked across Python automation, FastAPI, data engineering, trading systems, model evaluation workflows, and production support environments where correctness, auditability, and operational resilience matter. Recent work includes building trading infrastructure, evaluating LLM/agentic coding systems under realistic engineering constraints, and designing automation workflows for support, trading operations, and AI-assisted delivery. Core areas: Python, FastAPI, data pipelines, Parquet/DuckDB, RAG systems, LLM evaluation, agentic workflows, observability, trading infrastructure, customer support automation, and backend platform engineering.…

Michael Quaye

I'm a software engineer focused on backend engineering, AI systems, and data-intensive platforms. I build systems around LLMs, agentic workflows, trading infrastructure, and operational automation with an emphasis on reliability, observability, reproducibility, validation boundaries, and stakeholder-facing delivery. My background spans AI-enabled customer support automation, RAG/chatbot deployment, LLM and agentic coding evaluation, retail trading platform operations, market data pipelines, ETL, incident response, and backend/API development. I have worked across Python automation, FastAPI, data engineering, trading systems, model evaluation workflows, and production support environments where correctness, auditability, and operational resilience matter. Recent work includes building trading infrastructure, evaluating LLM/agentic coding systems under realistic engineering constraints, and designing automation workflows for support, trading operations, and AI-assisted delivery. Core areas: Python, FastAPI, data pipelines, Parquet/DuckDB, RAG systems, LLM evaluation, agentic workflows, observability, trading infrastructure, customer support automation, and backend platform engineering.…

Available to hire

I’m a software engineer focused on backend engineering, AI systems, and data-intensive platforms.

I build systems around LLMs, agentic workflows, trading infrastructure, and operational automation with an emphasis on reliability, observability, reproducibility, validation boundaries, and stakeholder-facing delivery.

My background spans AI-enabled customer support automation, RAG/chatbot deployment, LLM and agentic coding evaluation, retail trading platform operations, market data pipelines, ETL, incident response, and backend/API development. I have worked across Python automation, FastAPI, data engineering, trading systems, model evaluation workflows, and production support environments where correctness, auditability, and operational resilience matter.

Recent work includes building trading infrastructure, evaluating LLM/agentic coding systems under realistic engineering constraints, and designing automation workflows for support, trading operations, and AI-assisted delivery.

Core areas: Python, FastAPI, data pipelines, Parquet/DuckDB, RAG systems, LLM evaluation, agentic workflows, observability, trading infrastructure, customer support automation, and backend platform engineering.

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Skills

Experience Level

Freelance Gig
Expert
Python
Expert
PostgreSQL
Expert
SQL
Expert
Docker
Expert
Github
Expert
Kubernetes
Expert
AWS
Expert
HTML5
Intermediate
CSS
Intermediate
Redis
Intermediate
Node.js
Intermediate
React JS
Intermediate
TypeScript
Intermediate
Rust
Beginner
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Language

English
Fluent

Work Experience

Trading Operations Manager at Instant Funding
February 1, 2025 - October 1, 2025
Co-implemented the back-office architecture for IF Crypto, configuring trading accounts, groups, bridge connections, and liquidity links across MetaTrader 5, MatchTrader, and cTrader to enable live crypto trading and account funding operations. Built an OHLCV data ETL pipeline for 1-minute data across 450+ instruments (40GB+ total), retrieving data via exchange interfaces using Pandas, PyArrow, and Polars to boost speed by >95% and support 1k+ instrument configurations across three trading platforms. Led MT5 incident response across cross-region servers, diagnosing memory-exhaustion issues and driving capacity upgrades to restore performance. Audited and restored secure platform access for 3 users, centralized credential state management, and automated instrument configurations across platforms via a Python module, onboarding 1,000+ instruments with near-zero manual effort. Represented the firm in stakeholder engagements and coordinated with liquidity providers to ensure platform conne
Trading Platform Engineer at Instant Funding
February 1, 2025 - October 31, 2025
• Trading Infrastructure Setup: Co-implemented the back-office architecture for IF Crypto, configuring trading accounts, groups, bridge connections, and liquidity links across MetaTrader 5, MatchTrader, and cTrader to enable live crypto-asset trading and account funding operations. • OHLCV Data ETL Pipeline for Retail Trading Platforms: Built and maintained an ETL pipeline for 1- minute candlestick data across 450+ instruments (40GB+ total). Retrieved data via exchange interfaces using Pandas for smaller/reference datasets, PyArrow for file I/O, and Polars for CRUD on medium to large datasets, boosting speed by >95% (from est. 2880+ mins to <50 mins) with Jupyter demonstrations for team oversight. This pipeline transformed and loaded data into 3 trading platforms (cTrader, MatchTrader, MetaTrader 5) enabling 1k+ unique instrument configurations. • MT5 Trading Infra Access Recovery & Scaling: Led cross-region incident response and remediation across MetaTrader 5 trade, access, and backup servers, resolving a critical memory-exhaustion issue by coordinating capacity upgrades, while auditing and restoring secure access for other trading ops specialists (3 in total) through a centralised credential state store to stabilise operations and prevent recurrence. • Instrument Configuration Automation (Python): Achieved a 240x procedure improvement by building a Python library to generate instrument configurations across multiple trading platforms (MetaTrader 5, cTrader, Match-Trader) and a bridge system (Centroid). Implemented base instrument class with polymorphic behaviour to enable the quick and consistent onboarding of 1,000+ instruments (cryptos, commodities, FX, perp. futures, etc.), reducing manual effort + shortcuts by ~99.6% (from 4+ hours to <1 minute). • Stakeholder & Trading Platforms Lead: Represented the firm in engagements with stakeholders and C-level executives on trading and bridge platform integrations, architecture upgrades, and incident resolution; coordinated with a liquidity provider to support platform setup and connectivity; and served as the in-house trading platform authority for all departments (Customer Support, Compliance, Engineering, Marketing, etc.) across a ~50-person firm, leading onboarding, supporting on-site and remote teams, resolving platform issues, and escalating complex cases to vendor support.
AI Automation Engineer at Instant Funding
March 1, 2024 - October 1, 2025
Led the end-to-end SDLC for Voiceflow, Zendesk AI, Crisp, and Essel AI, delivering chat and email automation for customer support operations. Owned architecture, evaluation, deployment, optimization, and decommissioning across multiple platforms. Designed and implemented RAG pipelines in Voiceflow by defining intents, entities, and synonyms, cleaning and restructuring support policy documents, and enforcing guardrails to ensure policy-compliant model outputs. Built knowledge ingestion and curation components, validated 200+ Q&A pairs in Crisp, refactored 15K+ Zendesk tickets, and crawled 500+ pages to improve retrieval accuracy. Documented the system to be intuitive and self-explanatory for future engineers, improving onboarding and handover efficiency. Facilitated cross-team collaboration with Support, Engineering, and Marketing to align tone, escalation thresholds, and reporting standards, while implementing a reliability-first workflow that combines rule-based controls with LLMs for
Accounts & Funds Operations Lead at Instant Funding
March 1, 2024 - October 31, 2025
End-to-end risk and financial enforcement workflows, processing payouts for 600+ clients under margin thresholds, and leading dispute and chargeback processes across Stripe and Checkout (200+ cases resolved; £15k retained; 6 arbitration wins worth $7k). Implemented RAG and intent engineering for AI-powered support platforms, conducted structured UAT/regression testing, and established governance to improve reliability and auditability. Supported cross-team operations and governance across Support, Compliance, and Engineering; managed 7K+ client accounts and 5,000+ trading accounts.

Education

Add your educational history here.

Qualifications

Centroid Bridge Certified Professional
May 1, 2025 - May 1, 2026
Machine Learning with Python V2
January 12, 2025 - March 31, 2025

Industry Experience

Financial Services, Software & Internet, Professional Services
    CrMD Platform

    Building CrMD Platform, a cryptocurrency market data engineering system for ingesting, validating, storing, querying, and serving exchange market data across local and cloud-backed environments.

    The platform maps raw exchange API responses into typed OHLCV and funding-rate records, validates data at explicit service boundaries, and persists clean records as partitioned Parquet datasets. It uses PyArrow for decimal-safe columnar storage, DuckDB for in-place analytical querying over Parquet files, and row-level upsert merging to prevent duplicate records when overlapping historical ranges are re-fetched.

    The system supports pluggable exchange providers, concurrent multi-symbol ingestion, local Parquet storage, a cloud-agnostic StorageBackend abstraction, Azure Blob Storage with lease-protected writes, S3 and GCS backend support, CLI workflows, a FastAPI REST API with optional API-key authentication, local/remote SDK client modes, Docker packaging, benchmark tooling, and a Next.js/TypeScript web console for dataset inspection, tabular querying, and candlestick visualisation.

    Currently supports OHLCV and funding-rate data. OHLCV providers include stubs, Bitfinex, Bitstamp, KuCoin, Bybit, MEXC, and Gate.io.

    Key technologies: Python, FastAPI, Typer, DuckDB, PyArrow, Parquet, Azure Blob Storage, S3, GCS, Docker, Next.js, TypeScript, TanStack Query, Zod, and lightweight-charts.

    Project Link: https://www.twine.net/signin

    Documentation Sites:

    1. User Guides: https://www.twine.net/signin
    2. API Reference: https://www.twine.net/signin