Principal - Solution Architect

Role Summary • Customer-facing technical owner for opportunities and implementations. • Convert media, data and AI problems into secure, scalable and production-ready solutions. • Own architecture from discovery and pilot through engineering, deployment and handover. Key Responsibilities • Lead discovery with customer business, product, data, AI, security and engineering teams. • Define use cases, target workflows, architecture, integrations, data flows, non-functional requirements and roadmaps. • Preserve a reusable, model-agnostic core while allowing controlled customer configuration. • Assess data readiness, model fit, integration effort, cost, assumptions and delivery risks. • Support proposals and SOWs, and remain accountable through architecture reviews and production sign-off. AI, Platform & Cloud Architecture • Apply deep knowledge of video/multimodal AI, computer vision, speech AI, LLMs, embeddings, vector search, reranking, RAG, recommendations and agentic workflows. 
 • Turn model outputs into reliable decisions using metadata, ontology, business rules, confidence thresholds, ranking, temporal logic, human review and feedback. 
 • Define model evaluation across accuracy, precision, recall, relevance, temporal grounding, latency, throughput, explainability and cost. 
 • Design AI pipelines for ingestion, preprocessing, retrieval, inference, validation, feedback and continuous evaluation. 
 • Architect APIs, microservices, event-driven services, workflow engines, vector databases, model serving and enterprise integrations. • Integrate with MAM/DAM, CMS, media supply chain, data platforms, OTT/player, AdTech, localization and observability systems. • Define Hyperscaler deployment, Kubernetes/container patterns, security, resilience, observability, DR and cost controls. • Shape MLOps for model/prompt versioning, deployment, monitoring, drift detection, rollback, lineage and cost tracking. Hands-On & Collaboration • Create or review prototypes, APIs, notebooks, model evaluations, architecture diagrams and deployment designs. • Inspect payloads, logs, traces, model outputs and performance metrics to resolve issues with engineering teams. • Guide AI/ML, Data, Backend, Frontend, DevOps and QA teams on architecture and quality standards. • Partner closely with the Product Lead and Engineering Head on reusable capabilities and implementation sequencing. Required Experience & Qualifications 
 • 16- 20 years of technology experience, including Building and shipping AI Saas products / solutions , solution architecture, enterprise integration or cloud-native engineering ownership. 
 • At least 3–4 years of substantive AI/ML, data, automation or intelligent-platform architecture experience. 
 • Proven customer-facing experience leading discovery workshops, technical reviews and complex stakeholder discussions. 
 • Ownership of at least one solution from discovery or presales through pilot, engineering, deployment and handover. • Strong knowledge of distributed systems, APIs, microservices, event-driven architecture, data pipelines, CI/CD, observability and security. • Hands-on proficiency in Python, Java or JavaScript/TypeScript, with the ability to validate integrations and prototypes. • Experience with at least one major cloud platform and container/Kubernetes deployments. • Strong communication, estimation, problem structuring, risk management and architecture-documentation skills. • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or a related discipline. Preferred Media Experience • Media & Entertainment, OTT/streaming, broadcast, studios, MAM/DAM, AdTech, localization, live sports, QoE or video-processing workflows.