About Me
I am an AI/ML Engineer who excels at the intersection of analytical thinking and cutting-edge technology. With a PhD in International Affairs and technical engineering experience, I bring a unique perspective to artificial intelligence: I do not just build models; I build systems that are safe, reliable, and grounded in real-world complexity.
My path into AI began with a simple conviction: intelligent systems should enhance human capability, not replace it. That belief guides my work designing production-grade AI applications that combine rigorous analysis, responsible design, and thoughtful engineering. I specialize in building systems that integrate real-world context with powerful foundation models, applications that retrieve knowledge precisely, orchestrate multi-step reasoning, and deliver responses that are accurate, consistent, and aligned with the user's intent.
What Drives My Engineering
I am invested in the full AI application lifecycle. I enjoy the process of translating ambiguous problems into system architectures, evaluating whether retrieval or reasoning patterns are the right fit, designing context-aware pipelines, and building applications that actually solve business problems. My work often sits at the intersection of retrieval, agentic reasoning, and workflow automation, where models must operate with nuance, reliability, and transparency.
I am particularly passionate about Retrieval-Augmented Generation (RAG) because it blends model capability with authoritative, domain-specific knowledge. I have built retrieval pipelines that incorporate vector search, metadata filtering, hybrid strategies, and custom relevance scoring, ensuring that the model can surface the right information at the right time. In parallel, I design agent workflows that use tools, structured actions, and controlled orchestration to perform tasks that require multiple steps, context retention, and well-defined reasoning paths.
What excites me most is building AI systems that produce measurable impact, applications that reduce manual workload, streamline document-heavy processes, accelerate decision-making, and allow organizations to leverage their knowledge more intelligently.
My AI Engineering Philosophy
Application-Focused Design: Every system I build starts with understanding the actual user journey: their needs, constraints, edge cases, and the environment in which the AI will operate. I prioritize clarity in interaction patterns, predictable behavior, and user trust.
RAG as Core Infrastructure: I view RAG not as an add-on but as a foundational architecture that brings precision, transparency, and adaptability. I design retrieval layers that improve contextual grounding, reduce hallucinations, and support repeatable patterns across use cases.
Agentic Orchestration That Works: I build AI agents that can reason step-by-step, invoke tools, and interact with broader systems. Whether through MCP integrations, structured workflows, or multi-tool decision flows, my focus is on reliability, traceability, and controlled automation.
Prompt Engineering as an Engineering Discipline: Prompts are part of the system architecture. I approach them empirically, testing variations, analyzing failure modes, and documenting patterns that generalize.
Production Safety and Reliability: Responsible AI is non-negotiable. I design with guardrails, deterministic fallbacks, observability, safety checks, and cost/latency monitoring from day one. My systems include validation layers that ensure correct behavior before outputs reach the user.
Continuous Learning: I stay current with emerging patterns in agent design, retrieval optimization, and cloud-native AI systems. I engage with the growing engineering community, learn through experimentation, and apply new insights to every build.
