Jason Becht Engineering Organization Leader | Platform Strategy | Operational Turnaround | Reliability | AI Systems | Durable Execution Engineering leader and hands-on technologist with more than 20 years of experience building, modernizing, operating, and improving production software systems and the engineering organizations behind them. My background spans software engineering, platform strategy, distributed systems, infrastructure, reliability, engineering operations, modernization, organizational turnaround, and technical leadership. I have worked across the spectrum from hands-on engineering and Principal-level technical leadership through Acting Director responsibilities, consulting, fractional CTO work, company leadership, and executive technology advising. I tend to work where technical complexity and organizational complexity meet: systems that need to scale, platforms that need modernization, production environments that need to become reliable, teams that need clearer ownership and operating models, and emerging technologies that need to become durable production systems. I founded CrypTech Studios, where I spent more than two decades building software and advising organizations on technology strategy, and I am currently a Co-Founder of DigiMasq AI.

• Engineering organization leadership
• Platform strategy and platform engineering
• Operational turnaround
• Reliability and production engineering
• Distributed systems
• SaaS modernization
• Infrastructure automation
• Engineering operations
• Developer platforms and tooling
• AI systems and intelligent applications
• Technical strategy and durable execution

My recent work extends traditional software, platform, and distributed-systems engineering into AI-native systems.

I am particularly interested in the engineering around increasingly capable models: the architecture required to make AI reliable, stateful, context-aware, testable, observable, replaceable, and useful as part of a larger production system.

Areas I am actively working with include:

• Large Language Models and Vision-Language Models
• Agentic and tool-using systems
• Model Context Protocol (MCP)
• Context engineering and context management
• Retrieval-Augmented Generation (RAG)
• Embeddings and semantic retrieval
• Knowledge retrieval and grounding
• Structured generation and structured outputs
• AI orchestration
• Provider abstraction and capability routing
• Persistent and state-aware AI systems
• Deterministic and generative systems working together
• Multimodal workflows
• Image generation, editing, and visual pipelines
• Local and self-hosted AI infrastructure
• LoRA-based model adaptation
• Fine-tuning workflows
• Model evaluation and validation
• AI-assisted software engineering and development workflows

Technologies and concepts currently in that toolbox include:

LLMs · VLMs · MCP · RAG · Embeddings · Semantic Retrieval · Context Engineering · Tool Use · Agentic Workflows · Structured Generation · LoRA · Fine-Tuning · Model Evaluation · Multimodal AI · Ollama · ComfyUI

My view of AI is fairly simple:

The model is a capability. The surrounding system determines whether that capability becomes useful software.

Across engineering, platform, infrastructure, and operational leadership work, I have delivered outcomes including:

• Reduced high-severity production escalations by approximately 95%
• Reduced production MTTR from roughly 2–5 hours to 0.5–2 hours
• Reduced release-related production incidents by approximately 5–10x
• Directed modernization of 250+ production application instances
• Replaced an enterprise onboarding process representing roughly 60 engineer-weeks of manual effort with automation capable of onboarding 2–3 clients per day
• Led significant technical and operational initiatives across engineering organizations in the 37–65 person range
• Built and operated a technology consulting and product-development company for 21 years
• Served as a fractional CTO and executive technology advisor to SMB and enterprise organizations
• Designed and delivered SaaS platforms, integrations, infrastructure, automation systems, internal applications, and modernization programs across cloud and hybrid environments

The specific technologies have changed considerably over the years.

The underlying problems — complexity, ownership, reliability, change, scale, and execution — have not.

Python · Node · Ruby · Ruby on Rails · C# · .NET · JavaScript · TypeScript · React

Application architecture, APIs, backend services, integrations, automation, internal platforms, and production software systems.

PostgreSQL · Redis · Kafka · SQL

Distributed systems, event-driven architecture, service integration, data modeling, messaging, state management, and API contracts.

Linux · AWS · GCP · Kubernetes · Terraform · Ansible · CI/CD

Cloud and hybrid infrastructure, infrastructure automation, deployment systems, platform engineering, configuration management, production operations, observability, and modernization.

Production reliability, incident response, incident command, root-cause analysis, release governance, change management, observability, operational readiness, rollback strategy, service ownership, and recovery engineering.

IAM · SSO · OAuth2

Secure SDLC practices, access management, compliance-sensitive environments, production investigations, and security-conscious system design.

LLMs · VLMs · RAG · Embeddings · MCP · LoRA · Ollama · ComfyUI

AI orchestration, context engineering, retrieval-augmented generation, semantic retrieval, tool execution, provider abstraction, capability routing, structured generation, model adaptation and fine-tuning workflows, persistent AI state, multimodal systems, model evaluation, and local/self-hosted inference.

My leadership work has covered considerably more than people management.

I have operated across:

• Platform and engineering strategy
• Engineering operating models
• Organizational turnaround
• Production reliability
• Incident and escalation management
• Release and change governance
• Infrastructure modernization
• Delivery predictability
• Capacity recovery
• Technical ownership
• Vendor and technology selection
• Distributed and offshore engineering resources
• Cross-functional execution
• Engineering investment and cost decisions
• Mentoring engineers, technical leads, and engineering managers

I believe strong engineering leadership requires understanding both sides of the system:

the software being built and the organization responsible for building and operating it.

Good architecture reduces the cost of future decisions. It establishes clear responsibilities and boundaries without pretending we can predict every requirement the system will encounter.

Deployment, failure, recovery, observability, and operational ownership are architectural concerns — not tasks to hand to somebody else after development is finished.

Moving repetitive work somewhere else is not the same thing as removing it.

Good automation returns engineering capacity to the organization.

As systems grow, ambiguity becomes expensive. Clear contracts, responsibilities, sources of authority, and operational ownership matter.

You do not need to write every line of code to lead engineering effectively.

You do need enough technical depth to understand the consequences of the decisions being made.

A heroic recovery is useful once.

A better operating model prevents the organization from requiring the same heroics again.

More capable models do not eliminate the need for architecture, validation, state management, observability, deterministic boundaries, security, testing, and operational discipline.

They make those things more important.

A substantial portion of my career has involved commercial software, consulting engagements, enterprise systems, internal tooling, and proprietary platforms that cannot be represented by public GitHub repositories.

That work has included:

• SaaS products and platforms
• Business-critical backend systems
• Internal enterprise applications
• Infrastructure and deployment automation
• Customer onboarding platforms
• Workflow and operational systems
• Cloud and hybrid infrastructure
• API and third-party integrations
• Legacy modernization
• Production reliability programs
• Engineering platform development
• Technical and organizational transformation

Much of that source code remains private because of client confidentiality, employer ownership, commercial agreements, or intellectual-property considerations.

The repositories on this profile represent work that can be shared publicly — not the complete body of systems I have designed, built, operated, or led throughout my career.

My public work tends to reflect areas I am actively exploring or building in, including:

software engineering · developer tooling · infrastructure · platform systems · automation · AI systems · experimentation

Some of the projects I am currently working on remain private while they are under active development.

After more than two decades in software, I remain most interested in building things and figuring out how complicated systems should work.

Sometimes that means software.

Sometimes it means infrastructure or a platform.

Sometimes it means restructuring how an engineering organization operates.

And increasingly, it means figuring out how AI becomes a reliable part of real software systems without discarding everything we already know about architecture, state, reliability, operations, and good engineering.

• DigiMasq AI: digimasq.ai
• LinkedIn: linkedin.com/in/jason-becht