I build agentic AI systems that turn ambiguous business problems into governed workflows and measurable outcomes.
Builder of agentic AI and operations systems with deterministic automation, PRD-driven delivery, and CLI-agent-assisted coding workflow. Breaking problems into tasks, enforcing TDD, validating with Docker and curl.
Self-taught in FP&A and business analysis, bringing a strong business lens to internal tools, applied AI, and forward-deployed work.
What I Bring
Engineering capabilities paired with business analysis foundations for delivering production AI systems.
Engineering & Design
- Forward Deployed Engineering
- Solution Design
- Agentic AI Systems
- Workflow Orchestration
Development Stack
- Go 1.24 (Fiber, gRPC)
- Python (FastAPI, AsyncIO, Pydantic)
- LangGraph
- PostgreSQL, Redis, Qdrant
Methodology & Ops
- PRD Slicing
- Task Decomposition
- TDD
- Docker, Curl-based Smoke Tests
- Governance Gates
Reliability & Business
- Deterministic Logic
- Audit Trails
- HITL Review
- Langfuse Observability
- FP&A Variance Thinking
Delivery Stack
How I Work
Forward deployed engineering: integration, deployment, and production AI systems that deliver measurable business outcomes.
Integration-First
I connect AI capabilities to existing systems — APIs, data pipelines, and legacy infrastructure. The model is only useful if it fits the production environment.
Deployment Thinking
I design for production from day one: monitoring, edge cases, data sync, and graceful degradation. Staging success means nothing if it fails in the real environment.
Customer Problem Solving
I translate business pain into technical solutions and explain trade-offs to non-technical stakeholders. FDE is boundary work between engineering and business needs.
Production AI Systems
RAG, fine-tuned models, MCP servers, and multi-agent workflows. I build systems that take real actions, not just chat interfaces.
Documentation & Handoff
Architecture diagrams, deployment guides, and troubleshooting notes. Clear documentation is a force multiplier for engineering teams.
Measurable Business Outcomes
I optimize for throughput, accuracy, and cost. Every system ships with eval metrics, error modes, and a story about the improvement it delivers.
Delivery Stack
LangGraph · FastAPI · Go (Fiber, gRPC) · PostgreSQL + pgvector · Redis · Qdrant · Docker · Langfuse · TDD-first workflow
Selected Work
Agentic AI systems and operations platforms built to demonstrate production-grade engineering
Continuous Learning
Self-directed engineering, business analysis, and FP&A design focus areas.
Self-Directed Engineering, Business Analysis & FP&A Design
2023 – Present
Focus areas: Distributed Systems, Workflow Orchestration, Solutions Architecture, and Value Engineering
Why FP&A for an FDE
FDEs translate business pain into technical solutions. FP&A thinking — variance analysis, cost modeling, scenario reforecasting — lets me quantify the problem before building the system. A control tower that catches mismatches before payment blocks is worth more when you can express the dollar impact.
Why Business Analysis
BA skills — requirements elicitation, stakeholder mapping, process modeling — turn vague stakeholder asks into structured PRDs and granular task plans. The gap between "we need AI" and a deployed system is a requirements problem, not a model problem.
Microsoft Certified: Azure AI Associate / Fundamentals
Microsoft
Certified AI Agents Developer (HF-2025)
Hugging Face
AWS Educate Badge: Introduction to Generative AI
AWS
Google Cloud Partner Skills Boost
Google Cloud
Open to Forward Deployed Engineer & Applied AI roles
Seeking FDE and Applied AI positions where I can deploy AI systems into production, integrate with complex environments, and deliver measurable business outcomes.