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.

• 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.

• 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.

• 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.

• 9–10 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.

• Media & Entertainment, OTT/streaming, broadcast, studios, MAM/DAM, AdTech, localization, live sports, QoE or video-processing workflows.