AI Labs - Technical Product Architect

Key Responsibilities

1. Product & Technology Architecture

Own the end-to-end architecture and technology roadmap.

Define scalable, secure and modular architecture for video interviewing, AI interviewing, candidate assessment and recruiter workflows.

Design AI/ML architecture covering LLMs, speech-to-text, NLP, conversational AI, RAG, multimodal AI and AI-based evaluation.

Define architecture for real-time and asynchronous video/audio processing.

Drive API-first, microservices and cloud-native architecture.

Ensure scalability, availability, observability and performance for enterprise workloads.

2. Product Management

Translate customer/business requirements into product capabilities, architecture and roadmap.

Own/drive product backlog, feature prioritization and release planning in partnership with Product/Business teams.

Conduct competitive benchmarking against platforms such as HireVue, Spark Hire and other AI interviewing platforms.

Identify product gaps and define capabilities required to make products enterprise and market competitive.

Balance build vs. buy vs. integrate decisions for AI, video, communication and assessment components.

3. AI & Video Interviewing

The candidate should have strong understanding of:

AI interviewer / conversational AI

Dynamic and adaptive interview questioning

Job Description + Resume-based question generation

Speech-to-text and multilingual transcription

LLM-based interview summarization

Competency-based scoring and evaluation

Video/audio intelligence and multimodal AI

Interview scorecards and structured evaluation

AI-driven candidate ranking and recommendations

Anti-cheating / interview integrity capabilities

Human-in-the-loop AI evaluation

4. Enterprise Platform Capabilities

Drive architecture/productization of:

Multi-tenancy and tenant isolation

RBAC and enterprise identity integration

API gateway and partner integrations

ATS / HRMS integrations

Interview scheduling and notifications

Candidate and recruiter portals

Analytics and dashboards

Audit trails and data lineage

Consent, privacy and data retention

Security and compliance

Monitoring, SLA and production support architecture

5. Product Engineering Leadership

Work closely with Engineering, AI/ML, UX, QA, DevOps and Product teams.

Establish architecture standards and technical governance.

Review technical designs and ensure engineering alignment with product roadmap.

Mentor architects and senior engineers.

Drive technical debt reduction and platform modernization.

Required Skills

Architecture

Distributed systems / microservices

Cloud-native architecture AWS/Azure/GCP

REST APIs, event-driven architecture and API gateways

Databases, caching, messaging and data platforms

Kubernetes / containers

CI/CD, observability and SRE principles

AI

LLMs and GenAI

RAG and vector databases

Conversational AI / AI Agents

NLP and speech technologies

Multimodal AI

AI evaluation and model governance

Video / Communication

WebRTC or equivalent real-time video technologies

Video/audio streaming and processing

STT/TTS

Recording, storage and media pipelines

Experience with Twilio, Vonage or equivalent communication platforms is desirable.

Product

Product lifecycle management

Product roadmap and prioritization

Agile/Scrum

Customer discovery

Competitive analysis

Build vs. buy decisions

Enterprise SaaS product management

Preferred Experience

Candidates from AI-powered HRTech, Video Interviewing, Assessment, Recruitment SaaS, Conversational AI or Enterprise SaaS platforms will be preferred.

Experience working on products comparable to HireVue, Spark Hire, VidCruiter, Modern Hire or other AI/video interviewing platforms would be highly valuable.

Key Success Measures

The person should be able to:

Define the product and technology roadmap.

Build scalable architecture supporting enterprise customers and high-volume interviews.

Establish strong AI evaluation, security, privacy and governance capabilities.

Identify competitive gaps and convert them into prioritized product capabilities.

Reduce dependency on fragmented third-party components through a clear build/buy/integrate strategy.

Work across Product + Engineering + AI/ML + Business and drive execution.