Principal - Solution Architect

LTM

Required skills

Website: ltm.com
Job details:
Role Description

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 video-processing workflows.

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