LTM
Website:
ltminc.net
Company:
https://www.linkedin.com/company/ltm-larsenandtoubrocompany
Seniority: Not Applicable
Industries: IT Services and IT Consulting
Job details:
Role Description
Role Summary
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 modern 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.
- Define non-functional requirements covering performance, scalability, availability, security, privacy, observability, maintainability, interoperability and cost efficiency.
- Understand AI/ML engineering practices across data ingestion, feature and metadata pipelines, model integration, prompt and workflow versioning, evaluation, MLOps, monitoring, drift and incident diagnostics.
- Guide engineering trade-offs across speed, technical debt, extensibility and client commitments; ensure that short-term pilots do not compromise the reusable MediaCube core.
- Use AI-assisted product and engineering practices to accelerate discovery, requirements, prototyping, documentation, test-case creation, release analysis and competitive research.
- The role is not expected to be a full-time software developer; however, the Product Lead must be technically credible, comfortable reviewing technical artefacts and able to drive engineering decisions with depth.
Team & Operating Model
- Operate as an individual contributor with broad product authority and end-to-end accountability rather than as a large-team people manager.
- Direct and prioritize the work of the Business Analyst and Data Scientist supporting MediaCube, while ensuring clear outcomes, quality standards and timely decision-making.
- Provide day-to-day product direction to cross-functional contributors across Solution Architecture, AI/ML, Data Engineering, Backend, Frontend, QA, DevOps, Design, Delivery and Customer Success.
- Establish a disciplined product operating cadence covering discovery reviews, architecture reviews, backlog refinement, sprint outcomes, model evaluation, release governance and roadmap reviews.
- Drive alignment across Business, GTM, Consulting, Engineering and Delivery, and escalate decisions early when scope, architecture, quality, timeline or commercial expectations are at risk.
Preferred Experience & Qualifications
- 8–9 years of total experience across product management, technology products, enterprise software or AI/data platforms, with meaningful ownership of B2B or enterprise product outcomes.
- Demonstrated experience owning at least one complex product or platform through multiple stages of the lifecycle—from problem discovery and MVP/pilot through deployment, adoption and iteration.
- Strong technical product-management experience in AI/ML, data platforms, workflow automation, SaaS or cloud-native enterprise products; candidates limited to business coordination or project tracking will not be suitable.
- Deep practical understanding of AI product design and the limitations of AI systems, including data dependency, model quality, probabilistic outputs, evaluation, hallucination/error modes, latency, cost and governance.
- Strong understanding of software engineering and architecture fundamentals, including APIs, microservices, cloud platforms, event-driven systems, databases, data pipelines, CI/CD, observability and security.
- Ability to write precise PRDs and technical product specifications, define acceptance criteria and NFRs, interpret architecture diagrams and API documentation, and participate meaningfully in design and engineering reviews.
- Experience working closely with AI Engineers, Data Scientists, Solution Architects and software engineering teams using Agile product-development practices.
- Excellent problem structuring, prioritization, executive communication, client-facing presentation and stakeholder-management skills.
- Strong commercial judgement, including the ability to connect roadmap decisions to customer adoption, repeatability, pricing, cost-to-serve and revenue outcomes.
- Bachelor’s degree in Computer Science, Engineering, Data Science or a related technical discipline is preferred; equivalent evidence of strong technical depth will also be considered.
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