Senior Cloud Consultant AI Solutions Focus

Senior Cloud Consultant – AI Solutions Focus

• Role: Senior Cloud Consultant – AI Solutions Focus
• Primary Skill: Amazon Web Services (AWS) Cloud Computing
• Experience Required: 8–10 years
• Focus Areas:
  • AWS Cloud Architecture
  • Azure Cloud
  • Artificial Intelligence (AI)
  • Generative AI
  • Machine Learning
  • Large Language Models (LLMs)
  • Cloud-Native Solutions
  • Cloud Modernization
  • DevOps & Automation
  • AI Governance & Security

Role Overview

• Lead the design, implementation, and optimization of modern cloud-based applications.
• Serve as the AI and Cloud champion within the consulting team.
• Drive strategy, architecture, and delivery of intelligent applications using:
  • Generative AI
  • Machine Learning
  • Large Language Models (LLMs)
  • Cloud-native services
• Design scalable, secure, resilient, and innovative AI-driven solutions.
• Work closely with:
  • Business stakeholders
  • Product teams
  • Architects
  • Developers
  • Engineering teams
• Deliver measurable business value through AI and cloud transformation initiatives.

Cloud Architecture & Solution Design

• Lead the design and implementation of cloud-native applications and enterprise solutions on AWS, Azure, and other cloud platforms.
• Define:
  • Scalable cloud architectures
  • Secure architectures
  • Resilient architectures
  • Cost-optimized architectures
• Establish cloud design patterns, best practices, and governance standards.
• Evaluate and recommend cloud services, frameworks, and technologies based on business requirements.
• Collaborate with development teams to ensure successful solution delivery.
• Conduct architecture reviews.
• Provide technical leadership across multiple projects.
• Design solutions across:
  • Public cloud
  • Private cloud
  • Hybrid cloud
  • Multi-cloud environments

AI Enablement & Innovation

• Identify opportunities to leverage:
  • Artificial Intelligence
  • Generative AI
  • Machine Learning
  • Intelligent Automation
• Design and implement AI-enabled solutions using:
  • Amazon Bedrock
  • Amazon SageMaker
  • AWS AI Services
  • Azure OpenAI
  • Open-source LLM frameworks
• Develop:
  • AI use cases
  • Proofs of Concept (POCs)
  • Production-ready AI solutions
• Partner with stakeholders to assess AI feasibility, business value, and implementation strategies.
• Define architecture patterns for:
  • Retrieval-Augmented Generation (RAG)
  • AI Agents
  • Model orchestration
  • Enterprise search
• Promote responsible AI adoption.
• Establish AI governance, security, compliance, and ethical AI practices.
• Define AI model lifecycle management and risk management processes.

Cloud Consulting & Stakeholder Engagement

• Engage with business and technology stakeholders to understand strategic objectives.
• Assess existing IT landscapes and identify cloud adoption and modernization opportunities.
• Develop:
  • Cloud strategies
  • AI roadmaps
  • Cloud modernization roadmaps
  • Transformation strategies
• Conduct:
  • Cloud readiness assessments
  • Application discovery
  • Infrastructure discovery
  • Dependency analysis
  • Workload classification
  • Migration planning
• Facilitate:
  • Workshops
  • Architecture reviews
  • Technical discovery sessions
• Serve as a trusted advisor for cloud modernization and AI transformation.
• Create business cases and value realization strategies for AI investments.
• Recommend cloud migration, optimization, and modernization opportunities.
• Recommend appropriate:
  • Cloud services
  • Migration patterns
  • Deployment models
  • Build-versus-buy approaches

Cloud Migration & Modernization

• Define cloud adoption strategies and phased transformation roadmaps.
• Design cloud landing zones and target operating models.
• Lead or support:
  • Rehosting
  • Replatforming
  • Refactoring
  • Containerization
  • Data migration
  • Cloud-native modernization
• Support enterprise application and infrastructure migration initiatives.
• Coordinate application migration activities across technical teams.
• Facilitate:
  • Testing
  • Cutover
  • Production readiness
  • Knowledge transfer
  • Change management
  • Operational handover
  • Post-migration optimization

Platform Engineering & Automation

• Design and implement Infrastructure as Code (IaC) solutions using:
  • Terraform
  • AWS CDK
  • CloudFormation
• Build automated deployment pipelines and DevOps workflows.
• Implement CI/CD pipelines across cloud environments.
• Automate:
  • Infrastructure provisioning
  • Monitoring
  • Security controls
  • Compliance controls
  • Operational processes
• Support containerized and serverless workloads using:
  • Kubernetes
  • Amazon ECS
  • Amazon EKS
  • AWS Lambda
  • Azure Container Apps
• Establish configuration management and automated operational practices.

Security, Governance & Compliance

• Ensure cloud and AI solutions comply with organizational security policies and regulatory requirements.
• Implement:
  • Identity and Access Management
  • Encryption
  • Security monitoring
  • Observability
  • Access controls
• Define AI governance frameworks.
• Establish model lifecycle management processes.
• Implement AI risk management practices.
• Conduct architecture risk assessments.
• Develop remediation plans.
• Define requirements for:
  • IAM
  • Networking
  • Encryption
  • Backup
  • Disaster Recovery
  • Monitoring
  • Logging
  • Compliance
  • Operational resilience

Operational Excellence

• Monitor cloud solution:
  • Performance
  • Reliability
  • Availability
  • Cost efficiency
• Establish observability standards using:
  • CloudWatch
  • Grafana
  • Datadog
  • OpenTelemetry
• Support production incidents.
• Lead Root Cause Analysis (RCA) activities.
• Drive continuous improvement focused on:
  • Scalability
  • Reliability
  • Operational efficiency
  • Automation

Cloud Cost Management / FinOps

• Support cloud cost management initiatives.
• Define and implement:
  • Resource tagging
  • Budgeting
  • Forecasting
  • Rightsizing
  • Reservations
  • Chargeback
  • Cost optimization
• Design cost-efficient cloud architectures.
• Monitor cloud resource utilization and spending.

Required AWS Skills

• Strong expertise in AWS Cloud Services, including:
  • EC2
  • S3
  • EKS
  • ECS
  • Lambda
  • RDS
  • DynamoDB
  • API Gateway
  • VPC
  • IAM
  • CloudWatch
• Enterprise AWS architecture and migration experience.
• Experience with AWS security, networking, IAM, and cloud-native services.

Azure Skills

• Experience with Microsoft Azure cloud services.
• Experience with:
  • Azure OpenAI
  • Azure Container Apps
• Ability to design solutions across multi-cloud environments.

AI / Machine Learning Skills

• Experience implementing enterprise AI and Generative AI solutions.
• Knowledge of:
  • Amazon Bedrock
  • Amazon SageMaker
  • Azure OpenAI
  • OpenAI APIs
  • LangChain
  • LlamaIndex
  • Vector Databases
  • Semantic Search
  • RAG Architectures
  • AI Agents
• Understanding of:
  • Prompt engineering
  • Model evaluation
  • AI lifecycle management
  • Model governance
  • AI compliance
• Familiarity with LLMs such as:
  • GPT
  • Claude
  • Gemini
  • Llama
• Experience with Agentic AI frameworks and autonomous workflow orchestration.

Application Development Skills

• Experience supporting cloud-based applications and APIs.
• Proficiency in:
  • Python
  • Node.js
• Experience building and integrating:
  • REST APIs
  • Microservices
• Understanding of:
  • Event-driven architectures
  • Serverless architectures

DevOps & Automation Skills

• Terraform
• AWS CDK
• CloudFormation
• GitHub Actions
• Jenkins
• Azure DevOps
• CI/CD
• Docker
• Kubernetes
• Amazon EKS
• Amazon ECS
• Cloud automation
• Infrastructure as Code
• Configuration management
• Monitoring and observability

Qualifications

• Bachelor's degree in:
  • Computer Science
  • Engineering
  • Information Technology
  • Related field
• 8+ years of experience in:
  • Cloud Consulting
  • Cloud Architecture
  • Cloud Engineering
• 3+ years of experience delivering:
  • AI solutions
  • Machine Learning solutions
  • Generative AI solutions
• Proven experience designing and implementing enterprise-scale cloud solutions.
• Strong stakeholder management and consulting skills.
• Excellent:
  • Communication
  • Presentation
  • Problem-solving
  • Technical leadership
• Experience leading cross-functional technical initiatives.
• Experience mentoring engineering teams.

Preferred Qualifications

• AWS Solutions Architect Professional certification.
• AWS Machine Learning Specialty certification.
• Microsoft Azure AI Engineer certification.
• Experience building:
  • AI-enabled SaaS platforms
  • Enterprise AI applications
• Experience with Agentic AI frameworks.
• Experience with autonomous workflow orchestration.
• Knowledge of:
  • Data governance
  • Model governance
  • AI compliance frameworks

Project & Team Leadership

• Coordinate onsite and offshore teams.
• Manage:
  • Project milestones
  • Dependencies
  • Risks
  • Issues
  • Stakeholder expectations
• Provide clear executive-level status reporting.
• Collaborate with:
  • Architects
  • Developers
  • Infrastructure teams
  • Security teams
  • Data engineers
  • DevOps engineers
  • Product owners
  • Service providers