AI Solutions Architect (26-04315) -

AI Solutions Architect

The Mansion, 1050 Techwood Dr NW, Atlanta, GA 30318 (3 days onsite)

10 month Contract (possible for extension)

Team culture/work environment

• We have a small team split between ATL and NY.
• We are a technology team that does a lot of creative Proof of Concept work.
• We value the ability to take ownership of projects, take initiative, and build tools for creative industries.

You will have the unique opportunity to

• Build with cutting-edge AI models.
• Own ideas from concept to prototype.
• Solve real creative challenges across WBD.
• Collaborate directly with filmmakers and storytellers.

Must Have Skills / Requirements

• 3+ years architecting AI/ML or generative AI systems.

a. Strong experience with AI orchestration and workflow frameworks. Expert-level technical knowledge of architecture, application systems design, and integration at large scale, with demonstrated delivery of AI/ML systems into production.

• Familiarity of the Media and Entertainment workflows

a. Understanding of Production and Post-Production workflows from previous experience.

• Strong Understanding of SDLC

a. 3+ years; Ability to work with tools like Claude Code and Cursor.

Nice to Have Skills / Preferred Requirements

• Familiarity with AI governance and regulatory frameworks (e.g., NIST AI RMF, EU AI Act) and model evaluation methodology — preferred.
• Cloud architecture certification (AWS/GCP/Azure) or ML specialty certification a plus.

Soft Skills

• Forward-thinking about recommended services and anticipated business needs in a domain where the capability frontier moves quarterly.
• Strong understanding of data structures, pipelines, and governance — including training-data lineage, PII handling, and content rights management.

Technology Requirements

• Expert-level technical knowledge of architecture, application systems design, and integration at large scale, with demonstrated delivery of AI/ML systems into production.
• Deep hands-on experience with foundation models and generative

AI

LLMs, diffusion and video generation models, multimodal models; prompt engineering, RAG, fine-tuning and adapters (LoRA/PEFT), distillation, and agentic/tool-use patterns.

• Strong experience with model serving and inference infrastructure (e.g., vLLM, TensorRT-LLM, Triton, Ray, SageMaker, Vertex AI, Bedrock) and GPU capacity planning and cost optimization.
• Strong experience with AI orchestration and workflow frameworks.
• Experience with vector databases and retrieval systems (e.g., pgvector, Pinecone, Elasticsearch/OpenSearch, Milvus), embedding strategy, chunking, and hybrid search.

Strong experience with modern integration technologies

REST/gRPC APIs, event streaming (Kafka/Kinesis), webhooks, Model Context Protocol (MCP) and comparable agent-tool interfaces, and containerized deployment on Kubernetes.

Working knowledge of media-specific AI applications preferred

localization and dubbing, automated metadata and content tagging, editorial and promo assembly, VFX assist, archive search, QC and compliance review.

What We Do/Project

The Solutions Architect (SA) for the AI ContentLab is the highest-level domain technology expert for applied AI in content creation, production, and distribution. The SA is responsible for assessing business and creative requirements; collecting, identifying, and approving technical specifications; and developing AI/ML solutions that require engineering to be viable — including generative AI applications, model serving platforms, agentic and multi-agent workflow systems, fine-tuned and adapted foundation models, purchased AI platforms, and the integration of AI capabilities into existing production and business systems.

Job Responsibilities / Typical Day in the Role

• Serve as technical owner and point of contact for AI ContentLab solutions spanning generative media (text, image, video, audio, 3D), retrieval and knowledge systems, and agentic workflow automation.
• Develop technical specifications for AI systems and ensure new solutions are designed for optimal access, reusability, and measurable creative and business value.
• Apply architectural and engineering concepts to design solutions that meet operational requirements — scalability, maintainability, security, reliability, extensibility, flexibility, availability, and manageability — plus

AI-specific requirements

inference latency, throughput, cost-per-generation, GPU utilization, model accuracy and drift, output quality, and safety.

• Design and develop AI reference architectures that support corporate business strategy and the enterprise data and AI strategy, with focus on model platforms, media asset and metadata pipelines, vector and embedding stores, feature stores, and evaluation infrastructure.
• Assist in identifying which models, platforms, and vendors to employ based on benchmark performance, cost, licensing and IP indemnification terms, data-use and training-rights provisions, service agreements, and organizational constraints.
• Lead research and development efforts — proofs of concept, prototypes, model bake-offs, and red-teaming exercises — as subject matter expert when introducing new models, techniques, or tooling in a fast-moving field.

Define and own evaluation strategy

offline eval harnesses, golden datasets, human-in-the-loop review, A/B testing, and production quality monitoring for non-deterministic systems.

Architect responsible-AI controls into solutions by design

guardrails and content filtering, prompt-injection and data-exfiltration defenses, provenance and watermarking (e.g., C2PA), rights and talent-consent enforcement, audit logging, and human review gates.

• Provide input to management throughout the project management lifecycle (PMLC) and assist managers in developing SLAs and key performance measures appropriate to probabilistic systems (quality thresholds, refusal and escalation rates, cost per unit of output).
• Ensure AI solutions are production-ready, meet defined specifications, and can be maintained via production support methodologies and resources — including model versioning, rollback, retraining and re-evaluation cadence, and knowledge management.
• Facilitate communication between technology workgroups, creative and production teams, and business stakeholders on technical issues and major changes; translate model capabilities and limitations into terms non-technical partners can act on.
• Assume ownership of problems and incidents — including model degradation, harmful or off-brand output, and cost anomalies — and communicate critical situation status.

Education / Certifications

• Bachelor's degree in Computer Science, Machine Learning, Information Systems, or related major, or equivalent technical/business experience.

Interview Process / Next Steps

• 1st round technical interview (AI and Architecture) with Engineer (30-45 mins)
• 2nd round screening with Sr Director, AI Lab or another Director on the team. (30-45 minutes)