Generative AI Architect

Job description

About the Company:

Everest DX – We are a Digital Platform Services company, headquartered in Stamford.

Our Platform/Solution includes Orchestration, Intelligent operations with BOTs’, AI-powered analytics for Enterprise IT.

Our vision is to enable Digital Transformation for enterprises to deliver seamless customer experience, business efficiency and actionable insights through an integrated set of futuristic digital technologies.

Digital Transformation Services - Specialized in Design, Build, Develop, Integrate, and Manage cloud solutions and modernize Data centers, build a Cloud-native application and migrate existing applications into secure, multi-cloud environments to support digital transformation.

Our Digital

Platform Services enable organizations to reduce IT resource requirements and improve productivity, in addition to lowering costs and speeding digital transformation.

Digital Platform - Cloud Intelligent Management (CiM) - An Autonomous Hybrid Cloud Management Platform that works across multi-cloud environments.

helps enterprise Digital Transformation get most out of the cloud strategy while reducing Cost, Risk and Speed.

To know more please visit: http://www.Everestdx.Com

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:

1.

GenAI Solution Design

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

Design modular frameworks using:

• AWS Bedrock, SageMaker, and Comprehend.
• 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.

2.

Cloud & Infrastructure Architecture

Define scalable, secure infrastructure on AWS and Azure:

• Compute: ECS, EKS, Lambda, AKS, App Service.
• Storage & Data: S3, DynamoDB, Azure Blob, Cosmos DB, Synapse.
• 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.

3.

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.

4.

Security, Governance & Compliance

• Apply Responsible AI principles—bias 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.

5.

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.

Required Skill Profession

Computer Occupations