The Product Lead will own MediaCube end-to-end as a hands-on, technically strong individual contributor. The role combines product strategy, deep AI product knowledge,

architecture understanding and engineering execution to convert enterprise media problems into reusable, production-oriented AI capabilities. The Product Lead will define what MediaCube builds, why it matters, how it should work, how quality will be measured and how the solution progresses from discovery and pilot to repeatable deployment.

This is not a coordination-only product role. The incumbent must be able to engage deeply with Solution Architects, AI/ML Engineers, Data Scientists, Backend Engineers and client technology teams; challenge technical assumptions; make informed build-versus-buy and model-selection decisions; and drive engineering teams against clear functional and non-functional requirements.

MediaCube Product Mandate

· Own the product vision, strategy, roadmap and release priorities for the MediaCube

Intelligence Fabric and its reusable AI workflows.

· Productize MediaCube capabilities across video intelligence, content discovery,

semantic search, contextual advertising, highlights, localization, QoE intelligence and media supply-chain automation.

· Create a clear distinction between reusable MediaCube platform capabilities,

configurable client extensions and one-off custom engineering; continuously increase reuse across accounts.

· Build MediaCube as a model-agnostic orchestration and intelligence layer that can integrate cloud, proprietary and open-source models through well-defined adapters, APIs and evaluation standards.





· Convert client opportunities and consulting discoveries into scalable product capabilities without allowing the roadmap to become a collection of disconnected custom features.

End-to-End Product Ownership

· Lead product discovery with clients, sales, consulting and delivery teams; define the problem, target user, workflow, measurable outcome and commercial relevance before initiating a build.

· Translate business requirements into high-quality PRDs, workflow diagrams, epics,

user stories, acceptance criteria, data requirements, AI evaluation criteria and non-functional requirements.

Own prioritization and backlog decisions based on customer value, strategic fit,

reusability, technical feasibility, engineering effort, operational risk and revenue potential.

· Drive the complete lifecycle: discovery, solution framing, architecture alignment,

prototype, pilot, release readiness, deployment, adoption, feedback and roadmap improvement.

· Own release scope and readiness, including dependencies, risks, quality gates,

security and privacy requirements, observability, documentation, support readiness and rollback considerations.

· Represent MediaCube in executive reviews, client workshops, solution reviews,

roadmap discussions and product demonstrations.

AI, Data & Intelligence Responsibilities

· Partner with Model providers including negotiating token costs , workflow alignment ,

client requirements

· Demonstrate strong working knowledge of contemporary AI systems,



including multimodal and video AI, computer vision, speech and language AI, LLMs, embeddings, vector search, reranking, RAG, recommendation systems and agentic workflow patterns.

· Define how model outputs become reliable product decisions through metadata normalization, entity and ontology design, business rules, confidence thresholds, ranking logic, human-in-the-loop review and feedback loops.

· Partner with the Data Scientist to define data requirements, experiment design,

baselines, ground truth, evaluation datasets, offline and online metrics, error analysis and model-improvement priorities.

· Define AI quality and product metrics such as precision, recall, relevance,

confidence, latency, throughput, cost per workflow, explainability, user acceptance and business outcome.

· Evaluate models and partners on fit-for-purpose performance, cost, latency,

deployment constraints, security, extensibility and vendor lock-in; drive informed build,

buy, partner or fine-tune decisions.

· Ensure responsible AI practices, including traceability, data privacy, model governance, auditability, human oversight and appropriate handling of model uncertainty and failure modes.

Technology & Engineering Knowledge

· Possess sufficient engineering depth to review and challenge system architecture,

API contracts, data models, event flows, integration patterns, model-serving approaches and deployment designs.

· Work closely with Solution Architecture and Engineering to shape scalable, modular and production-oriented designs using APIs, microservices, event-driven patterns, cloud services, data pipelines and containerized deployment

📌 Product Lead (India)
🏢 AstraaTech
📍 India