Description

Key Responsibilities

AI & GenAI Architecture

      •     Define and implement enterprise AI architecture frameworks, standards, and best practices.

      •     Design end-to-end Generative AI solutions leveraging Claude, Gemini, OpenAI, and other leading foundation models.

      •     Architect Retrieval Augmented Generation (RAG), agentic AI, multi-agent systems, vector databases, and knowledge management solutions.

      •     Develop scalable AI platforms supporting enterprise-wide adoption and governance.

Cloud & Platform Architecture

      •     Architect AI solutions across Azure and AWS environments.

      •     Design secure and scalable AI infrastructure utilizing:

      ◦     Azure OpenAI Service

      ◦     Azure AI Foundry

      ◦     Azure Machine Learning

      ◦     Azure Cognitive Services

      ◦     Amazon Bedrock

      ◦     Amazon SageMaker

      ◦     AWS AI Services

      •     Establish cloud-native patterns for AI application deployment and operations.

Solution Design & Delivery

      •     Lead technical architecture reviews and solution design workshops.

      •     Translate business requirements into scalable AI and machine learning solutions.

      •     Collaborate with engineering teams to implement production-grade AI systems.

      •     Drive AI adoption through reusable frameworks, accelerators, and reference architectures.

Governance & Responsible AI

      •     Define AI governance, security, compliance, and risk management frameworks.

      •     Implement model monitoring, observability, guardrails, and responsible AI practices.

      •     Ensure solutions comply with enterprise security, privacy, and regulatory requirements.

Leadership & Innovation

      •     Serve as a trusted advisor to executive leadership on AI strategy and emerging technologies.

      •     Mentor architects, engineers, and data science teams.

      •     Evaluate emerging AI technologies and recommend adoption strategies.

      •     Drive innovation initiatives and proof-of-concepts for strategic business opportunities.

Required Qualifications

Experience

      •     10+ years of experience in software engineering, cloud architecture, data platforms, or enterprise architecture.

      •     5+ years of experience designing and implementing AI/ML solutions.

      •     3+ years of experience with Generative AI and Large Language Models in enterprise environments.

      •     Proven experience leading enterprise AI transformations and architecture programs.

Technical Expertise

Foundation Models & GenAI

      •     Demonstrated expertise with:

      ◦     Anthropic Claude

      ◦     Google Gemini

      ◦     OpenAI models

      ◦     Multi-model orchestration strategies

      •     Experience with prompt engineering, fine-tuning, model evaluation, and AI agent frameworks.

Microsoft Azure

      •     Azure OpenAI Service

      •     Azure AI Foundry

      •     Azure Machine Learning

      •     Azure Kubernetes Service (AKS)

      •     Azure Data Services

      •     Azure Security and Identity solutions

Amazon Web Services

      •     Amazon Bedrock

      •     Amazon SageMaker

      •     AWS Lambda

      •     ECS/EKS

      •     AWS Data and Analytics services

      •     AWS Security and Governance services

AI & Data Technologies

      •     RAG architectures

      •     Vector databases (Pinecone, Weaviate, Milvus, Chroma, Azure AI Search)

      •     LangChain, LangGraph, LlamaIndex

      •     AI Agents and Agentic Workflows

      •     MLOps and LLMOps

      •     Model monitoring and evaluation frameworks

Programming

      •     Python

      •     SQL

      •     REST APIs

      •     Microservices Architecture

      •     CI/CD and DevOps practices

Preferred Qualifications

      •     Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.

      •     Experience with enterprise AI governance and Responsible AI frameworks.

      •     Experience in regulated industries such as healthcare, financial services, defense, or technology.

      •     Experience integrating AI solutions with ERP, CRM, and enterprise systems.

Preferred Certifications

      •     Microsoft Certified: Azure Solutions Architect Expert

      •     Microsoft Certified: Azure AI Engineer Associate

      •     AWS Certified Solutions Architect Professional

      •     AWS Certified Machine Learning Specialty

      •     Google Professional Cloud Architect

      •     Google Professional Machine Learning Engineer

Key Skills

Education

Bachelor's degree