Generative AI Solution Architect

Job Title : GenAI Solution Architect (AWS & Azure).

Job Location : Hyderabad /Chennai.

Job Type : Full Time.

Experience : 10 years (including 3 years in AI/ML or GenAI architecture).

Notice Period Immediate to 15 days joiners are highly preferred.

Key Responsibilities :

GenAI Solution Design :

- Architect end-to-end Generative AI solutions on AWS and Azure.

- Design modular frameworks using :

a. AWS Bedrock, SageMaker, and Comprehend.

b. Azure OpenAI Service, Azure Machine Learning, and Cognitive Search.

- Build Retrieval-Augmented Generation (RAG) pipelines integrating vector databases (e.g., Amazon OpenSearch, Pinecone, Azure Cosmos DB embeddings).

- Design prompt orchestration using frameworks such as LangChain, Semantic Kernel, or CrewAI.

Cloud & Infrastructure Architecture :

- Define scalable, secure infrastructure on AWS and Azure :

a. Compute : ECS, EKS, Lambda, AKS, App Service.

b. Storage & Data : S3, DynamoDB, Azure Blob, Cosmos DB, Synapse.

c. Networking & Security : VPC, Private Link, IAM, Key Vault, KMS, Defender for Cloud.

- Implement Infrastructure-as-Code (IaC) using Terraform or Bicep.

- Leverage API Gateway / Azure API Management for secure AI API deployment.

AI/ML Lifecycle & MLOps :

- Architect AI pipelines across training, tuning, and inference stages.

- Integrate CI/CD for AI workflows using GitHub Actions / Azure DevOps Pipelines.

- Design monitoring and governance for LLM outputs, model drift, and data quality.

- Support fine-tuning and prompt optimization using Bedrock model customization or Azure OpenAI fine-tuning endpoints.

Security, Governance & Compliance :

- Apply Responsible AI principlesbias detection, interpretability, and data privacy.

- Implement compliance controls aligned with GDPR, SOC2, ISO 27001, and NIST.

- Secure data and models using AWS KMS, Azure Key Vault, and Private Endpoints.

- Enforce RBAC and least privilege across all AI service components.

Collaboration & Stakeholder Management :

- Partner with Product Owners and Data Leaders to identify GenAI business use cases.

- Collaborate with developers, data scientists, and cloud engineers for delivery.

- Provide architecture governance, design reviews, and hands-on mentoring.

- Present GenAI solution roadmaps and PoC outcomes to business executives.

Required Skills & Qualifications :

Technical Expertise :

years as a Cloud Architect or AI/ML Solution Architect.

- Expertise in :

AWS AI/ML Stack : Bedrock, SageMaker, Comprehend, Rekognition, Lambda.

Azure AI Stack : Azure OpenAI, Azure Machine Learning, Cognitive Search, Synapse, Form Recognizer.

- Solid understanding of LLMs, embeddings, prompt engineering, and RAG architectures.

- Strong hands-on in Python and AI orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel).

Cloud & DevOps Skills :

- Experience with Terraform, CloudFormation, or Bicep.

- Proficiency in Docker, Kubernetes (EKS/AKS), and serverless design patterns.

- Knowledge of CI/CD, GitOps, and observability (CloudWatch, Log Analytics).

Security & Governance :

- Deep understanding of cloud-native identity, encryption, and compliance frameworks.

- Experience implementing AI guardrails and output moderation workflows.

Soft Skills :

- Excellent communication and stakeholder engagement skills.

- Ability to translate complex AI architectures into business value propositions.

- Thought leadership in cloud-native AI adoption and digital transformation.

Preferred Certifications :

- AWS Certified Solutions Architect Professional.

- Microsoft Certified : Azure Solutions Architect Expert.

- Microsoft Certified : Azure AI Engineer Associate.

- OpenAI / DeepLearning.AI Generative AI Certification (nice-to-have).

Nice-to-Have Experience :

- Designing multi-cloud AI pipelines across AWS and Azure.

- Implementing GenAI copilots, chatbots, or knowledge assistants.

- Using LangGraph, CrewAI, or Dust for AI agent orchestration.

- Integration of Power BI, Power Platform, or AWS QuickSight for AI analytics.

KPIs / Success Metrics :

- Successful design and deployment of scalable GenAI architectures across AWS & Azure.

- Reduction in model deployment and iteration times.

- Improved model performance, governance, and cost efficiency.

- Alignment of GenAI solutions with enterprise compliance and business objectives.

- Stakeholder satisfaction and measurable GenAI ROI.