Zulfadhli Mohamad
Senior AI/ML Engineer (PhD) | 7+ years working with audio, video, image & text | Production ML, MLOps & Personalisation | Startup experience
About Me
I'm a Senior Machine Learning Engineer (PhD) with 7+ years building production ML systems across audio, video, image and text. I led ML for a startup product called Rotor Videos acquired by LyricFind used by 600K+ users and partnered with major music labels. I've published audio signal‑processing and ML research during my PhD. Currently I focus on production-ready agentic workflows and generative AI for scalable music‑video production.
Specializations
Multimodal AI for media: media analysis, generative pipelines, recommendation & personalisation, and production-grade MLOps.
My Strengths
- Multimodal AI/ML (audio, video, image, text) and media analysis - Production ML & MLOps: scalable pipelines, deployment, monitoring (GCP, Docker, Kubernetes) - Generative systems & prompt engineering for media workflows - Recommendation, personalisation, and feature‑engineering for user impact - Product‑focused: metrics, A/B experimentation, cost/latency optimisation - PhD‑level technical depth + startup pragmatism
What You'll Gain
- Fast, measurable improvements in product metrics (engagement, conversion, retention) - Production‑ready ML components that are robust, observable and cost‑efficient - Clear roadmap from prototype to scalable deployment with maintainable handoffs - Practical guidance on combining analysis and generation in media products - Accelerated team capability via code, docs and mentoring - Better hiring decisions: role profiles, candidate evaluation criteria, interview guides, and interview support to help you recruit the right ML talent.
Topics I Cover
- Media analysis: audio feature extraction, vision pipelines, lyrics/NLP - Multimodal model design, representation learning and fusion strategies - Generative pipelines: diffusion, VAE/decoder approaches, LLM orchestration & prompt design - Recommenders, personalisation and style transfer for media - MLOps: data pipelines (Dataflow/Beam), training at scale, CI/CD, monitoring, infra as code - Evaluation: KPIs, A/B test design, human‑in‑the‑loop validation and safety guardrails
What to Expect
- Outcome‑driven scoping with clear KPIs and milestones - Rapid prototyping (MVPs) to validate assumptions before full build - Hands‑on implementation followed by production hardening and monitoring setup - Collaborative work with product, design and engineering teams and clear documentation/handover - Transparent timelines, risk assessment, and iterative delivery with measurable checkpoints
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