Senior Cloud Consultant – AI & Cloud-Native Solutions

• Job Title: Senior Cloud Consultant - AI & Cloud-Native Solutions
• Location: Indianapolis, IN
• Duration: 6 months
• GBaMS ReqID:
• Experience Required: 8+ years in cloud consulting, architecture, or cloud engineering
• AI Experience: 3+ years delivering AI, ML, or Generative AI solutions

• Job Title: Senior Cloud Consultant - AI & Cloud-Native Solutions
• Location: Indianapolis, IN
• Duration: 6 months
• GBaMS ReqID:
• Experience Required: 8+ years in cloud consulting, architecture, or cloud engineering
• AI Experience: 3+ years delivering AI, ML, or Generative AI solutions

Role Overview

• Senior Cloud Consultant specializing in Artificial Intelligence and Cloud-Native Solutions.
• Serve as the AI and Cloud champion within the consulting team.
• Lead strategy, architecture, implementation, and optimization of intelligent cloud applications.
• Expertise in:
• Generative AI
• Machine Learning
• Large Language Models (LLMs)
• Cloud-native services
• Deep expertise in AWS, with experience across Azure and other cloud platforms.
• Work with business stakeholders, product teams, architects, and developers to deliver scalable and secure AI-driven solutions.
• Provide technical leadership, cloud architecture, AI solutioning, governance, and hands-on implementation.

Cloud Architecture & Solution Design

• Lead design and implementation of cloud-native applications and enterprise solutions on AWS, Azure, or other cloud platforms.
• Design scalable, secure, resilient, and cost-optimized cloud architectures.
• Establish cloud design patterns, best practices, and governance standards.
• Evaluate and recommend cloud services, frameworks, and technologies.
• Collaborate with development teams for successful solution delivery and operational excellence.
• Conduct architecture reviews and provide technical leadership across multiple projects.

AI Enablement & Innovation

• Identify opportunities for:
• Artificial Intelligence
• Generative AI
• Machine Learning
• Intelligent automation
• Design and implement AI solutions using:
• Amazon Bedrock
• Amazon SageMaker
• AWS AI Services
• Azure OpenAI
• Open-source LLM frameworks
• Develop AI use cases, proof-of-concepts, and production-ready solutions.
• Assess AI feasibility, business value, and implementation strategies.
• Define solutions for:
• Retrieval-Augmented Generation (RAG)
• AI Agents
• Model orchestration
• Enterprise search
• Promote responsible AI adoption, governance, security, compliance, and ethical AI practices.

Cloud Consulting & Stakeholder Engagement

• Engage business and technical stakeholders to understand strategic objectives.
• Translate business objectives into cloud and AI roadmaps.
• 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.

Platform Engineering & Automation

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

Security, Governance & Compliance

• Ensure cloud and AI solutions comply with organizational security policies and regulatory requirements.
• Implement:
• Identity and Access Management
• Encryption
• Observability
• Security monitoring
• Define AI governance frameworks.
• Establish model lifecycle management and AI risk management practices.
• Conduct architecture risk assessments and remediation planning.

Operational Excellence

• Monitor cloud solution:
• Performance
• Reliability
• Cost efficiency
• Establish observability standards for monitoring and logging.
• Support production incidents and lead root cause analysis (RCA).
• Drive continuous improvement focused on:
• Scalability
• Reliability
• Operational efficiency

Required Cloud Skills

• Deep expertise in AWS, including:
• EC2
• S3
• EKS
• ECS
• Lambda
• RDS
• DynamoDB
• API Gateway
• VPC
• IAM
• CloudWatch
• Experience with Microsoft Azure.
• Enterprise cloud architecture and migration experience.

Required AI / ML Skills

• Experience implementing enterprise AI and Generative AI solutions.
• Experience/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
• Familiarity with foundation models such as:
• GPT
• Claude
• Gemini
• Llama

Application Development Skills

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

DevOps & Automation Skills

• Terraform
• CloudFormation
• AWS CDK
• GitHub Actions
• Jenkins
• Azure DevOps
• CI/CD
• Docker
• Kubernetes
• Grafana
• Datadog
• OpenTelemetry
• CloudWatch

Qualifications

• Bachelor's degree in:
• Computer Science
• Engineering
• Information Technology
• Related field
• 8+ years of cloud consulting, architecture, or engineering experience.
• 3+ years of AI, Machine Learning, or Generative AI delivery experience.
• Proven experience designing enterprise-scale cloud solutions.
• Strong stakeholder management and consulting skills.
• Excellent communication, presentation, and problem-solving skills.
• 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 and enterprise applications.
• Experience with Agentic AI frameworks and autonomous workflow orchestration.
• Knowledge of:
• Data governance
• Model governance
• AI compliance frameworks

Key Success Measures

• Successful delivery of scalable, secure, AI-enabled cloud solutions.
• Increased adoption of AI across business applications.
• Improved operational efficiency through automation and intelligent workflows.
• Measurable business outcomes from AI and cloud transformation.
• High stakeholder satisfaction and trusted-advisor relationships.
• Establishment of reusable cloud and AI architecture standards.

Key Resume Keywords

• Senior Cloud Consultant
• Cloud Architect
• AWS
• Azure
• Cloud-Native
• Generative AI
• Artificial Intelligence
• Machine Learning
• LLM
• Amazon Bedrock
• SageMaker
• Azure OpenAI
• OpenAI API
• LangChain
• LlamaIndex
• RAG
• Retrieval-Augmented Generation
• AI Agents
• Agentic AI
• Vector Database
• Semantic Search
• Prompt Engineering
• Python
• Node.js
• REST API
• Microservices
• Terraform
• AWS CDK
• CloudFormation
• Kubernetes
• Docker
• EKS
• ECS
• Lambda
• CI/CD
• DevOps
• CloudWatch
• Grafana
• Datadog
• OpenTelemetry
• AI Governance
• Cloud Security
• Cloud Migration
• Enterprise Architecture