Description

Job Summary:

We are looking for an experienced Generative AI Architect to define and drive the organization's enterprise Generative AI strategy and architecture.

The role will focus on designing scalable RAG platforms, multi-agent AI systems, cost-efficient LLM inference infrastructure, and enterprise AI governance frameworks.

The ideal candidate will have strong experience in Generative AI, LLM architecture, RAG, vector databases, model serving, cloud AI platforms, and enterprise AI governance, along with the ability to provide technical leadership across AI initiatives.

Key Responsibilities:

GenAI Strategy & Platform Architecture Define the client's multi-year Generative AI blueprint, including technology strategy, architecture roadmap, and platform capabilities.

Develop model selection strategies covering open-source and proprietary models based on performance, cost, scalability, security, and business requirements.

Establish governance standards for GenAI frameworks, models, development practices, and enterprise adoption.

Design enterprise-grade Retrieval-Augmented Generation (RAG) architectures using high-throughput vector databases such as Pinecone, Milvus, and pgvector.

Architect RAG solutions integrating enterprise data sources, including video telemetry, content metadata libraries, and customer data platforms.

Architect multi-agent orchestration systems using frameworks such as LangGraph, AutoGen, and Semantic Kernel for complex, multi-step workflows.

Define scalable architecture patterns for LLM applications, retrieval systems, agents, tools, and AI workflows.

Infrastructure & Cost Optimization Design cost-efficient and low-latency LLM inference pipelines using cloud AI platforms such as: AWS Bedrock Azure OpenAIGoogle Cloud Vertex AIDefine architecture strategies for scalable model deployment and inference.

Implement GPU utilization and resource optimization strategies to improve infrastructure efficiency.

Design and implement model optimization techniques including: Model quantization Caching layers Token usage optimization Efficient inference Workload optimization Establish strategies to manage and optimize enterprise AI compute and operational costs.

Evaluate model-serving technologies such as NVIDIA Triton and vLLM for high-performance inference workloads.

Security, Ethics & Governance Establish enterprise-level GenAI guardrails and responsible AI frameworks.

Design mechanisms for: Hallucination detection Toxicity filtering Data Loss Prevention (DLP) Output validation Secure LLM interactions

Define governance standards for model usage, data access, AI application development, and production deployment.

Ensure AI solutions comply with Digital Rights Management (DRM), CCPA/user privacy, and copyright requirements.

Establish appropriate controls when ingesting and processing: Video scripts Captions User logs Other enterprise content and dataEnsure AI architectures support secure, compliant, and responsible use of enterprise data.

Cross-Functional Technical Leadership Partner with Product Managers, Data Engineering teams, and Content Operations to translate business requirements into viable Generative AI initiatives.

Evaluate proposed AI use cases and define appropriate technical architectures and implementation approaches.

Provide technical mentorship to GenAI Engineers.

Conduct architecture and code reviews to ensure engineering quality and adherence to established standards.

Create and maintain Architecture Decision Records (ADRs) documenting key technology and architecture decisions.

Promote engineering best practices across GenAI development and deployment teams.

Qualifications & Requirements:

Education: Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.

Equivalent practical experience with demonstrated expertise in Generative AI architecture may also be considered.

Experience10+ years of experience in software engineering, data architecture, machine learning, or related technical disciplines.

At least 3+ years of experience specifically leading Generative AI architecture, LLM deployment, and RAG systems at scale.

Proven experience designing enterprise-grade AI platforms and production LLM solutions.

Experience providing technical leadership and mentoring AI engineering teams

Key Skills

Education

Any Graduate