AI Systems Engineer & LLM Reliability Specialist
I build production-ready AI systems, from LLM pipelines and multi-agent architectures to scalable SaaS platforms and APIs, with a focus on reliability, structured reasoning, and performance under real-world conditions. I turn raw prompts into fully deployed products that deliver meaningful business impact.
My approach: building AI systems that are measurable, reliable and genuinely useful, not just impressive demos.
I'm Christian Chuks (ChuksForge) — an AI Systems Engineer focused on designing and deploying production-grade systems. I develop end-to-end LLM-powered applications, from evaluation pipelines to multi-tenant SaaS backends, with an emphasis on performance, scalability, and real-world usability.
My background in systematic trading, spanning price action, forex and crypto, shapes how I approach AI: prioritizing signal over noise, building for robustness under uncertainty, and relying on data-driven decision making.
Current work spans a multi-tenant commerce assistant, a trading-grade data analytics platform, and a suite of multi-agent developer tools designed for real CI/CD workflows. Production standard, not proof-of-concept.
Click any project to expand architecture details, key contributions, and technical decisions.
Built a multi-agent code review system that addresses the core failure mode of LLM-based review tools: noisy, repetitive, low-priority findings that slow down rather than accelerate development. A critic loop re-evaluates initial LLM outputs against static analysis results, deduplicates findings, and produces a ranked, structured report suitable for direct CI/CD consumption.
Built a LangGraph-powered research agent that goes beyond standard RAG by combining live web search with vector-backed document retrieval, then running both through an evaluation layer before synthesis. The agent plans its own research strategy, identifies gaps in retrieved evidence, and iterates until it reaches a confidence threshold. Producing reports grounded in both real-time and stored knowledge.
Built a fully autonomous VC research pipeline that takes a company name as input and produces a structured investment memo; covering market sizing, competitive landscape, founder signals, financial indicators, and risk factors. Each domain is handled by a specialised agent and a synthesis agent reconciles outputs into a final, human-readable report.
Developed a research synthesis system that solves a specific problem with standard RAG pipelines: they retrieve and summarise, but don't reason across sources. LexisAI extracts discrete claims, tags each with source attribution and claim type (fact, inference, general knowledge), cross-compares for contradictions, and ranks outputs by evidential strength. Producing reports that are traceable, not just plausible.
A six-agent system built with the raw Anthropic SDK that replicates a hedge fund research process. Agents specialise in market data, news sentiment, technical signals, and fundamental analysis. Opposing "bull" and "bear" agents stress-test each thesis before a final decision agent synthesises a clear action with confidence level and risk context.
Production-ready SaaS platform with multi-tenant architecture, secure API proxying, embeddable web widget for fashion merchants, and a demo experience for prospects. Deployed on modern cloud infrastructure.
A production-grade SaaS monorepo that packages multiple AI applications, including an email assistant, RAG chatbot, workflow builder, and multi-agent operator, under a single shared platform layer. Each app is independently deployable but shares auth, billing, job queuing, and observability infrastructure. Designed to demonstrate enterprise-level AI product architecture at scale.
An LLM-powered career coaching agent that evaluates career pivot viability, maps skills gaps, and generates a structured action plan. Designed to replace shallow advice with grounded, role-specific guidance across any industry combination.
A task management agent that applies dynamic prioritisation logic. Combining urgency, impact, and effort scoring, to surface what actually deserves attention. Demonstrates structured tool use and output generation in a practical, non-trivial domain.
A composable SaaS platform built on a Turborepo monorepo housing a content generation engine and a career development toolkit. Designed with shared auth, billing, and API infrastructure so each tool is independently deployable without duplicating platform logic.
A professional trading analytics platform with CSV import, exchange API sync, live candlestick charting with entry/exit markers, R-multiple tracking, and multi-pair equity curves. Designed for systematic traders who need data, not opinions.
Capstone project from the prompt-engineering-lab tying together document ingestion, retrieval-augmented QA, hallucination mitigation strategies, and a multi-model benchmarking dashboard. Demonstrates the full stack of applied prompt engineering from raw document to trusted output.
A structured research portfolio covering the full spectrum of applied prompt engineering — from evaluation benchmarks to hallucination mitigation.
Founded and lead a registered AI engineering consultancy delivering production-grade LLM systems, multi-agent architectures, and AI SaaS platforms for startups and growth-stage companies. Operating at the intersection of applied AI research and scalable product engineering, with a focus on reliability, scalability, and real-world deployment constraints.
Apply discretionary and rule-based strategies in high-noise, probabilistic environments across forex and crypto markets.
Executed a structured research initiative exploring LLM behavior, reliability, and system integration patterns.
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I'm open to freelance projects, consulting engagements, and full-time AI system engineering roles. Let's talk.
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