Personal details
Muhammad J.
Technical skills
About
AI Systems Architect and Engineering Lead with 10+ years of experience designing and delivering scalable, production-grade systems, recently focused on RAG architectures, agentic workflows, and AI-powered platforms.
Combines the reliability and engineering rigor of working with organizations like Motorola Solutions with hands-on experience building autonomous systems, retrieval pipelines, and LLM-powered products in production environments.
Has led small engineering teams (3–6 engineers), owned technical direction and delivery, and built systems end-to-end, from data ingestion and retrieval to agent orchestration and user-facing applications.
Currently concluding ongoing projects and seeking a long-term role with ownership, where I can contribute to building and scaling AI-native products.
Core focus:
- AI Systems Architecture (RAG, retrieval pipelines, agentic workflows)
- Distributed systems & microservices
- Production-grade LLM integrations & guardrails
- Observability, reliability & performance at scale
- Engineering leadership & delivery ownership
Leadership & Operating Style
- Managed and mentored 3–6 engineers
- Owned technical direction, delivery, and architecture decisions
- Contributed to hiring and performance feedback
Operating style:
- Async-first, low-meeting environment
- Strong documentation and ownership culture
- Focus on outcomes over activity
- Balanced roadmap: ~70% features / 30% tech debt
Work Experience
- Architected a centralized data warehouse (Google BigQuery) integrating Shopify Plus, Google Ads, and supplier APIs across a 1M+ SKU catalog, enabling unified data access and analytics at scale
- Built Python-based autonomous agents to dynamically adjust pricing and inventory tagging based on real-time sales signals and business rules
- Designed and implemented LLM-powered enrichment pipelines, transforming raw manufacturer data into structured, high-converting product content at scale
- Owned end-to-end API integrations across Shopify, Google Merchant Center, and external supplier systems, ensuring data consistency and reliability across the platform
- Developed agentic workflows combining retrieval, validation, and deterministic output generation, improving data quality and automation reliability
Contributed to scaling a $12M+ revenue engine through AI-driven pricing optimization and performance tooling
- Architected and maintained high-scale microservices (Python, Node.js) handling 2M+ daily requests
- Ensured 99.9% uptime via Kubernetes orchestration and optimized database performance
- Designed and implemented LLM-powered systems and agent-like workflows, enabling contextual reasoning, tool execution, and intelligent automation
- Built RAG pipelines combining structured + behavioral data for semantic retrieval and personalization
- Developed AI recommendation engine for real-time adaptive user interactions in production systems
- Engineered memory and context-aware systems for long-running conversational and behavioral personalization flows
- Built Python-based AI infrastructure for validation, monitoring, and feedback loops, improving reliability by 40%
- Implemented observability and evaluation pipelines (Grafana, Kibana) for tracking AI behavior and system performance
- Supported full-stack delivery using Node.js, TypeScript, React, Express.js for APIs and user-facing systems
Technologies: Python, Node.js, TypeScript, Flask, Django, Express.js, PostgreSQL, React, Redux Toolkit, Vue 2, Docker, Kubernetes, Grafana, Kibana, OpenAI API, Anthropic Claude API, LangChain, RAG pipelines, vector search concepts, JWT, MSAL