SRE
SCALE & STABILIZE
Jaltencolead
Apply on the employer's siteRole description
Got a working prototype, but production feels like a dark art?
We plug in, clean it up, and ship it reliably. You build fast. We make it survive reality.If any of these sound familiar... you’re in exactly the right place.I don't know how to deploy this reliably.Login, payments, or email handling is a mess.It's slow, crashes often, or randomly times out.My database design feels sketchy and unscalable.I'm scared to update it because I might break prod.Cloud costs are random and completely confusing.Security, auth, and GDPR compliance freak me out.I need it to run 24/7 for real, paying users.Your fragile MVP transformed into a scalable ecosystem in 5 risk-free steps.Secure connection established. Agents bypass legacy UI, interfacing directly with core systems.►We connect your prototype to a proper production setup — safely.Manual workloads transferred. Employees input commands via high-speed neural-like interfaces.►We automate deploys and rollbacks so you can ship updates without stress.Data liberation initiated. New records write to modern, owned infrastructure.►We copy data safely and test changes without risking your live users.Mass extraction. Autonomous agents map and transfer technical debt in hours.►We move you off the fragile setup and onto something scalable — without downtime.Legacy systems offline. Architecture 100% future-proofed. Control reclaimed.►If anything goes wrong, we can instantly revert. You’re never trapped.How innovative teams use MCP to strangulate legacy monoliths—saving millions while moving faster.Why do large companies move so slowly, even when the limits of expensive, sluggish SaaS platforms are obvious? It is rarely because they do not see the problem. More often, it comes down to a mix of risk optimization, talent scarcity, and legacy complexity."No one gets fired for buying IBM" still applies, now in the form of heavyweight enterprise platforms and long-term vendor contracts. If a custom-built stack fails, leadership owns the consequences. If a major SaaS vendor underdelivers, there is always a contract, an SLA, and a procurement decision to point to. Most companies are comfortable buying software. Far fewer are comfortable owning their own engine of change.Building an agent-driven stack does not require more people who can click through outdated admin interfaces. It requires teams that understand AI orchestration, integrations, data models, and how to build secure workflows on top of existing systems. That talent is expensive, scarce, and unevenly distributed, which leaves traditional enterprises moving slower than the market around them.Many companies are sitting on 10 to 15 years of tangled content, special-case logic, and ERP integrations. As a result, they assume modernization requires a massive platform replacement. In practice, this is often where AI agents create the most leverage: accelerating the mapping, structuring, cleanup, and migration of data step by step, without forcing the business into a risky all-at-once rewrite.Innovative companies rarely do big-bang migrations. They strangle the monolith step by step, moving workflows out first and letting the platform shift happen in the background. This is the playbook for bypassing legacy software without forcing the business into an all-at-once rewrite.Connect agents to legacy APIs and turn the old system into a data source rather than the place where work actually happens. The result is that teams can move away from the slow, bloated enterprise UI without ripping out core systems on day one.Let agents take over the workflows and write all new content to your own modern, lightweight database. The legacy platform stays alive in the background, but it gradually loses its role as the system where real work gets done.Once the new path carries enough of the operational load, AI can be used to map, clean, and migrate legacy data at scale. At that point, the company can cut the multi-million-dollar license surface, keep the infrastructure that still matters, and move control back into its own stack.Turn the old CMS into a passive data source and build an MCP server on top of it. That gives agents a way to read, search, and work against the content without requiring people to operate through the legacy interface.Let a modern orchestration layer take over the day-to-day workflows. AI agents can power dynamic MCP widgets, meaning task-specific forms and interfaces, so marketing teams can review, adjust, and approve campaigns without opening the legacy system.Write all new content to your own modern database, such as Postgres or Supabase, and mirror back only what is necessary to keep the legacy environment functioning during the transition.Once the shadow database has enough operational weight, agents can be used to map, structure, clean, and migrate years of content debt far faster than traditional migration projects. What once required months of manual effort can often be compressed dramatically.When users no longer rely on the old interface and the dependency on the platform has dropped far enough, the frontend can begin talking directly to the new agent-driven data layer. At that point, the legacy system is no longer the center of gravity, only a remaining integration until it can be fully shut down.Legacy CMS platforms often present "complex versioning and localization" as a reason to stay locked in. In practice, versioning, review flows, and localization are straightforward to support with MCP and modern orchestration layers, without carrying the weight of a legacy platform.We move durable content out of expensive CMS databases and into a Git-backed Markdown/MDX system your team and agents can review, version, search, remember, and roll back.Editors and agents can still use friendly interfaces, but every durable change lands as a file change, commit, branch, review, and rollback point. That gives agents a visible wiki-style memory they can read, cite, and propose updates to.Markdown keeps long-lived knowledge visible. Agents can use the same files as durable memory, then propose pull requests when the company knowledge changes.Pull pages, docs, policies, product copy, and help content from the legacy CMS.Normalize the content into Markdown/MDX with front matter and commit it to your repo.Build the live site, search index, embeddings, and optional cache DB from Git.Let humans and agents propose changes through previews, diffs, and pull requests.Markdown/MDX files, front matter, branches, diffs, reviews, and commits.Rendered pages, search indexes, embeddings, analytics, permissions, and runtime caches.Start by moving the durable content layer. Keep runtime systems where they still make sense.We do not just prompt LLMs. We deploy a multi-model orchestration pipeline built on .NET 10 and Azure that grounds AI in your business in a controlled, repeatable way.Our service layer abstracts the model provider. Tasks are routed to the right model, whether that is Groq, OpenAI, Gemini, or local Ollama, based on cost, latency, and capability. You are never trapped inside a single ecosystem.We replace brittle multi-database setups with native vector capabilities in platforms such as Azure Cosmos DB, PostgreSQL, or SQL Server. Documents, metadata, and embeddings stay synchronized in one scalable data layer.Memory retrieval is not an optional side call. It is a defined part of the pipeline. We query the vector store, re-rank the results based on recency and relevance, and inject the right context before the model even sees the prompt.We don't just consult; we build. LovOne.ai is our public direct-to-consumer flagship product, powered natively by the same Context Engine architecture we deploy for enterprise clients.LIVE PRODUCT // OPEN TO VISITORSLovOne.ai is now public: a private memorial-room experience where people can gather stories and photos, then return to gentle AI conversations grounded only in what they choose to share.We design branded ChatGPT app experiences that turn AI conversations into discovery, guided choice, and qualified handoff.Customers are learning to ask instead of search. That turns ChatGPT into a new distribution surface for lightweight branded experiences, guided decision flows, and early qualification before deeper product logic takes over.Most brands are still absent in the moment customers ask AI what to choose. That is where early attention is moving.Discovery is shifting from links and search results into conversations.For the right journeys, ChatGPT becomes the interface for lightweight branded interaction.Brands that move now gain a new distribution surface before the channel gets crowded.The journey begins in a high-intent conversation, not on a landing page or in an app marketplace.Customers get a guided branded experience instead of generic text and a list of links.Users compare, explore, and understand fit before they click away.Traffic moves into your own funnel when the customer is ready to act.Most companies still treat ChatGPT as text. Use it as a channel before that window closes.Clear, bounded scopes for production systems and new AI-native channels.Fixed scope to get you live safely.For apps breaking under real user load.Ongoing fractional DevOps & Architecture.Launch a branded discovery surface inside ChatGPT.Move durable content and agent memory into Git-backed Markdown/MDX.Secure channel open. Send us your parameters.SYSTEM.LOG // 2026 © VISIONS IN CODE. SYNC COMPLETE.
This is a saved copy of a posting published elsewhere. Postings get taken down without notice — check the employer's site before applying. mentors.coach is not the hiring party.