EverestDX - 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 : - 5+ 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. (ref:hirist.tech)