I turn complex technical content into structured, high-quality learning experiences. That means taking business goals, job-task requirements, SME input, platform constraints, and learner needs — and producing the full range of deliverables: course architecture, learning objectives, video scripts, readings, labs, assessments, and learner-facing copy.

I have developed content across 90+ courses and 950+ learning assets on Coursera and edX, spanning AI, cloud computing, hybrid and multicloud systems, DevOps, cybersecurity, and software development.

Alignment discipline. Every piece I produce — objectives, instruction, practice, assessment — must connect. I verify that technical claims are supported, that the learning sequence builds logically, and that what learners are tested on matches what they were actually taught.

That discipline scales from a single video script to a 9-course professional certificate.

I am not a software engineer, and I do not present myself as one. I am a learning professional who works effectively inside technical environments. I collaborate with engineering and data science SMEs, work directly with source code, lab environments, scripts, and platform tooling, and ask the kinds of questions that surface gaps before they reach the learner.

My recent work spans deep learning, PyTorch, generative AI for NLP, and hybrid cloud architecture — all produced in close partnership with technical subject matter experts.

I developed two structured methodologies for AI-assisted instructional design: MADE™ (Map · Architect · Discern · Enable) and AIDE™ (Analyze · Instruct · Discern · Execute). MADE™ governs the overall content design process. AIDE™ governs how I use AI tools within that process.

Both are built on the same principle: the human leads, the AI assists.™ In practice, I use AI to accelerate drafting, content analysis, restructuring, and alignment checking — within a workflow that includes defined review gates, quality controls, and validation steps at every phase.

I treat quality as a system, not a checklist. I check for unsupported claims, unclear wording, weak transitions, missing steps, objective-assessment mismatches, and places where learners are likely to get stuck.

For AI-assisted content, I add a specific layer of review for hallucinations, vague explanations, inconsistency, and overclaiming. The more complex the deliverable, the more structured the review process becomes.

Three things in combination: technical range, alignment discipline, and a named, repeatable methodology for using AI in content production.

Many instructional designers can write clearly. Many can use AI tools. Few have built structured frameworks that govern both the design process and the AI-assisted workflow, and fewer still have applied those frameworks at scale across hundreds of production assets.

Yes, and I prefer environments that require both. I can define a course architecture, map a full specialization, or design a content strategy. I can also write the video script, QA the lab instructions, and refine the assessment items.

At IBM, I contributed to seller enablement, knowledge platform content, and internal communications across a 15-year career. In my current role, I architect courses and produce individual assets within the same sprint cycle.

I parse content into "chunked learning" that addresses the learner's needs. I identify the core concepts, the logic of the process, what the learner truly needs to know at this level, and where confusion is most likely to occur. Then I build the content so the learner can follow the reasoning, not just memorize the output.

That approach has held up across topics from residual networks in deep learning to spatial transcriptomics in life sciences.

I have worked in matrixed environments my entire career — with SMEs, project managers, designers, writers, publishers, QA reviewers, voiceover artists, and business stakeholders. I know how to ask SMEs precise questions, translate their expertise into learner-appropriate language, and manage the review cycle so technical accuracy and instructional clarity both survive.

I am used to real production constraints: templates, platform rules, brand standards, deadlines, and multi-stakeholder review cycles.

I invest in working relationships. Whether I am collaborating with someone for the first time or the fiftieth, I want that person to feel respected, valued, and set up to do their best work.

In practice, that means I prepare before meetings so shared time goes to decisions, not discovery. I adapt my communication to the role — technical shorthand with SMEs, status clarity with project managers, clean handoffs with QA reviewers. When stakeholders give direction, I listen carefully, apply their feedback, and close the loop so they know their input shaped the work. I work ahead of the sprint so blockers surface early, not at deadline. And I work comfortably across time zones, keeping momentum through asynchronous handoffs without letting the human connection drop.

People I work with tend to notice a few things: I look out for my teammates, I follow through on what I commit to, and I make collaboration easier, not harder.