AI Architect
Summary
The AI Architect will lead the design and implementation of enterprise-scale AI and Generative AI solutions, serving as the technical authority for AI initiatives. The role involves defining architecture roadmaps, establishing governance standards, and mentoring teams using technologies like LLMs, RAG, cloud platforms, and MLOps.
We are seeking an experienced AI Architect to lead the design, architecture, and implementation of enterprise-scale Artificial Intelligence and Generative AI solutions. The ideal candidate will possess deep expertise in AI/ML technologies, Large Language Models (LLMs), cloud-native architectures, and modern software engineering practices.
This role will serve as the technical authority for AI initiatives, partnering with business leaders, product teams, data scientists, and engineering teams to define AI strategies, establish architecture standards, and deliver scalable, secure, and production-ready AI solutions.
Key Responsibilities
AI Strategy & Architecture
- Define and drive the organization's AI and Generative AI architecture roadmap.
- Design end-to-end AI platforms and enterprise AI solutions aligned with business objectives.
- Establish best practices, governance standards, and architecture frameworks for AI adoption.
- Evaluate emerging AI technologies and recommend suitable solutions for enterprise use cases.
Solution Design
- Architect AI-powered applications utilizing:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- AI Agents & Agentic Workflows
- Natural Language Processing (NLP)
- Machine Learning & Deep Learning
- Design scalable and resilient AI platforms on Azure, AWS, or GCP.
- Define integration patterns with enterprise applications, APIs, data platforms, and cloud services.
Technical Leadership
- Provide architecture guidance to AI Engineers, Data Scientists, and Development teams.
- Lead solution reviews, proof-of-concepts, and technology evaluations.
- Drive engineering excellence, security, scalability, and performance optimization.
- Mentor engineering teams on AI architecture patterns and best practices.
Governance & Security
- Establish Responsible AI, model governance, security, and compliance frameworks.
- Ensure AI solutions adhere to regulatory, privacy, and cybersecurity requirements.
- Define monitoring, observability, and model lifecycle management strategies.
Stakeholder Management
- Engage senior stakeholders and business leaders to identify AI opportunities.
- Translate business requirements into scalable architecture solutions.
- Present AI strategies, solution designs, and technical recommendations to leadership teams.
Required Qualifications
- Bachelor's Degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or related disciplines.
- 8-15+ years of technology experience with a minimum of 3-5 years in AI/ML architecture or solution architecture roles.
- Proven experience designing and delivering enterprise-scale AI or Generative AI solutions.
- Strong software engineering and cloud architecture background.
- Experience leading architecture discussions with senior business and technology stakeholders.
Technical Skills
Artificial Intelligence & GenAI
- Large Language Models (LLMs)
- Generative AI
- Retrieval-Augmented Generation (RAG)
- Prompt Engineering
- Fine-Tuning Models
- AI Agents / Agentic AI
- NLP
- Deep Learning
- Machine Learning
AI Frameworks
- LangChain
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- OpenAI SDK
- Hugging Face
Cloud Technologies
- Azure OpenAI
- Azure AI Foundry
- Azure Machine Learning
- AWS Bedrock
- Amazon SageMaker
- Google Vertex AI
Data & Platform Technologies
- Databricks
- Snowflake
- Apache Spark
- Kafka
- SQL / NoSQL Databases
- Vector Databases (Pinecone, Weaviate, ChromaDB, Milvus)
Engineering & DevOps
- Python
- Java (Preferred)
- REST APIs
- Docker
- Kubernetes
- Terraform
- CI/CD
- MLOps