Mikhail T.

Forward Deployed Engineer | AI Agents & Automation | Claude Code

Forward Deployed Engineer (FDE) for AI products, AI agents, automation, and production software. I work directly with the product owner and take one bounded problem from discovery and architecture through build, deployment, and production ownership. Most software delivery is not slow because developers type code slowly. It is slow because work waits. A product owner explains the problem to an analyst. The analyst turns it into a spec. A PM coordinates it. A developer builds it. DevOps deploys it. Every handoff is another queue — and another place where context gets lost. I work differently. As a Forward Deployed Engineer, I work directly with the person who owns the business problem and take responsibility for the whole path: Discovery → Specification → Architecture → Build → Integration → Deployment → Production ownership. No spec ping-pong. No ticket factory. No one in the middle translating what the product owner meant. WHAT I BUILD • AI agents and agentic workflows connected to real business systems • Business process and workflow automation • Internal tools and operational software • Document and data processing systems • API, CRM, SaaS, and third-party integrations • AI features inside existing products • Production-ready replacements for AI/vibe-coded prototypes • Cloud infrastructure and deployment around the systems I build HOW I USE AI AI is not valuable because it types code faster. The bigger advantage is context. Instead of starting every task from a five-line ticket, I use the available product context — requirements, decisions, architecture, previous discussions, constraints, and current system state — to keep implementation connected to the original business problem. Claude Code, MCP, OpenAI, Gemini, and other agentic tools help me cover more ground without splitting the problem across a larger team. The engineering judgment stays human. PRODUCTION, NOT DEMOS Shipping the first version is only part of the job. I also care about: • secure secrets handling • least-privilege access • isolated environments • Dockerized deployments • logging and operational visibility • failure and edge-case handling • maintainable code and documentation • deployment and rollback procedures • a written runbook for whoever owns the system next Stack includes Claude Code, MCP, OpenAI, Gemini, AWS, GCP, Azure, DigitalOcean, Docker, Tailscale, Cloudflare Tunnel, and WireGuard. WHEN FDE WORKS BEST The best engagement starts with one bounded product or business workflow and one person on your side who can make product decisions. Give me direct access to the problem, the relevant systems, and the person who owns the outcome. I take it from there. WHEN I AM NOT THE RIGHT FIT If your real bottleneck is five mandatory departmental approvals, adding a faster engineer will not solve it. If nobody owns the product and every decision still needs a committee, an FDE will end up waiting in the same queues as everyone else. I would rather identify that before we start. If you have a product, internal system, AI initiative, or workflow that moves much slower than it should, send me a message describing: 1. What needs to happen 2. Who currently touches it 3. Where it usually gets stuck I’ll tell you whether it is a good FDE-shaped problem and what I would carve out as the first production scope.