Technical Program Manager · AI
The role asks for
My experience shows
Drive end-to-end delivery of LLM and agentic AI programs across engineering, data, risk, and business stakeholders.
01
Currently owns technical delivery of conversational AI and digital human products at Blits.ai.
Coordinate vendors, platform APIs, STT/TTS providers, data pipelines, and inference infrastructure.
02
Drives build/buy/partner decisions across frontier models, inference, data, STT, and client platform APIs.
Own model evaluation, pre-production risk review, and executive sign-off for regulated use cases.
03
Produces vulnerability assessments and LLM risk reports that support go/no-go decisions.
Architect the technical solution behind the programme, not only its schedule.
04
Architects multi-LLM systems with conversational and tool-use layers, including agentic payment flows.
Keep executives, engineering, and compliance aligned as delivery risk emerges.
05
Aligns client executives, product, engineering, and compliance teams around AI capabilities and delivery risk.
Set the evaluation standard a programme is measured against.
06
Leads Blits.ai's LLMs4EU evaluation workstream, defining metrics and methodologies with European partners.
AI Product Manager
The role asks for
My experience shows
Define the problem and the user before the spec — turn ambiguous market signals into a product thesis.
01
Defined kimkim's market-entry approach for new destinations — which markets to open and why — before building.
Set success metrics up front, then read post-launch signal to decide what ships next.
02
Set metrics and read the signal: removed 60% of manual workflows and grew bookings 3× after shipping integrations and activity scoring.
Make product decisions under ambiguity — scope, sequencing, and stack choice without complete information.
03
Chose the AI stack under ambiguity — model, inference, STT/TTS, and data — through build/buy/partner calls and A/B testing.
Treat governance as product surface — evaluation, guardrails, and sign-off users can trust.
04
Built the evaluation suite as a product with users: defined metrics and methodologies via LLMs4EU that gate go/no-go.
Convert discovery into a prioritized roadmap and OKRs across engineering, data, and commercial.
05
Set strategic roadmaps and OKRs at kimkim, analyzing results to prioritize what to build next.
Own the product end to end, from problem definition to shipped, monitored production.
06
Shipped agentic flows end to end at Blits.ai — payment, catalog search, and tool-use across a multi-LLM stack.
Director / Head of AI Product
The role asks for
My experience shows
Set AI product direction across roadmap, architecture, partnerships, governance, and commercial value.
01
Provides forward-looking product strategy on dialect NLP gaps, avatar inference cost, and agentic architecture trade-offs.
Make build/buy/partner calls across the agentic AI stack and vendor ecosystem.
02
Owns build/buy/partner decisions across model selection, inference, data pipelines, STT, and integrations.
Define product evaluation, risk posture, and adoption path for regulated customers.
03
Defines LLM evaluation benchmarks and risk methodologies through LLMs4EU.
Design product surfaces that integrate with regulated enterprise systems.
04
Architects product flows across payment APIs, CRM, core banking systems, and real-time merchant integrations.
Turn client discovery into reusable requirements, patterns, and delivery strategy.
05
Turns banking requirements into scalable partnership frameworks and repeatable AI delivery playbooks.
Own roadmap, OKRs, and commercial outcomes for a product line.
06
Previously owned roadmaps, OKRs, product evolution, content strategy, UX analytics, and commercial growth at kimkim.