Solution Architect, AI Platform

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 •

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.

Preferred Media Experience

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