AI Product Strategy & Consumer Feature Engineering

Overview

Designed and delivered multiple AI-driven product features and strategies across consumer SaaS — spanning a media AI expansion, an AI-powered job application platform, and first-line support automation. Each project combined product thinking with hands-on engineering to produce measurable business outcomes.

Achievements

• Media AI: built the business case for 8 fal.ai photo/video features competing with $200M+ revenue standalone products — projecting 7–37x ROI in Month 1 at $200–$1,000/mo infrastructure cost, with +1–2% subscription conversion lift. • Job Platform: reduced time-to-apply from 2–3 hours to under 5 minutes via an end-to-end AI pipeline (job scraping, CV analysis, role-tailored CV/cover letter generation, auto-apply, AI interview coach) — achieving 3x higher interview callback rates. • Support Automation: delivered 60–80% reduction in Tier-1 support ticket volume with positive ROI within 90 days, using a RAG system over 280+ knowledge base articles with automated hallucination detection.

Responsibilities

• Verified all model pricing and availability independently — correcting estimates up to 50x too high, preventing broken launches, and producing board-ready strategy documents with ICE-scored prioritisation.
• Built multi-agent architectures (LangGraph, CrewAI) for job matching, interview coaching, and support triage — reducing manual workload by 60–80% with 24/7 autonomous operation.
• Engineered cost-optimised hybrid AI workflows with model routing between cloud and local LLMs (DeepSeek, Llama 3), cutting inference costs by ~40% without accuracy regression.

Technologies Used