Assistant Vice President- Generative AI-System Architect

of Assistant Vice

Role Overview:

You will be responsible for end-to-end architecture of Gen.AI systems including model lifecycle management, inference, orchestration, pipelines

Key Responsibilities:

• Architect and design end-to-end systems for production-grade Generative AI applications (e.g., LLM-based chatbots, copilots, content generation tools).

• Define and oversee system architecture covering data ingestion, model training/fine-tuning, inferencing, and deployment pipelines.

• Establish architectural tenets like modularity, scalability, reliability, observability, and maintainability.

• Collaborate with data scientists, ML engineers, platform engineers, and product managers to align architecture with business and AI goals.

• Choose and integrate foundation models (open source or proprietary) using APIs, model hubs, or fine-tuned versions.

• Evaluate and design solutions based on architecture patterns such as Retrieval-Augmented Generation (RAG), Agentic AI, Multi-modal AI, and Federated Learning.

• Design secure and compliant architecture for enterprise settings, including data governance, auditability, and access control.

• Lead system design reviews and define non-functional requirements (NFRs), including latency, availability, throughput, and cost.

• Work closely with MLOps teams to define the CI/CD processes for model and system updates.

• Contribute to the creation of reference architectures, design templates, and reusable components.

• Stay abreast of the latest advancements in GenAI, system design patterns, and AI platform tooling.

Qualifications we seek in you!

Minimum Qualifications

• Proven experience designing and implementing distributed systems, cloud-native architectures, and microservices.

• Deep understanding of Generative AI architectures, including LLMs, diffusion models, prompt engineering, and model fine-tuning.

• Strong experience with at least one cloud platform (AWS, GCP, or Azure) and services like SageMaker, Vertex AI, or Azure ML.

• Experience with Agentic AI systems or orchestrating multiple LLM agents.

• Experience with multimodal systems (e.g., combining image, text, video, and speech models).

• Knowledge of semantic search, vector databases, and retrieval techniques in RAG.

• Familiarity with Zero Trust architecture and advanced enterprise security practices.

• Experience in building developer platforms/toolkits for AI consumption.

• Contributions to open-source AI system frameworks or thought leadership in GenAI architecture.

• Hands-on experience with tools and frameworks like LangChain, Hugging Face, Ray, Kubeflow, MLflow, or Weaviate/FAISS.

• Knowledge of data pipelines, ETL/ELT, and data lakes/warehouses (e.g., Snowflake, BigQuery, Delta Lake).

• Solid grasp of DevOps and MLOps principles, including containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, and model monitoring.

• Familiarity with system design tradeoffs in latency vs cost vs scale for GenAI workloads.

Preferred Qualifications:

• Bachelor's or Master's degree in computer science, Engineering, or related field.

• Experience in software/system architecture, with experience in GenAI/AI/ML.

• Proven experience designing and implementing distributed systems, cloud-native architectures, and microservices.

• Strong interpersonal and communication skills ability to collaborate and present to technical and executive stakeholders.

• Certifications in cloud platforms (e.g., AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, Google Cloud Professional Data Engineer).

• Familiarity with data governance and security best practices.