Senior Technical Consultant - AI Platform Engineer

We are seeking a hands-on Senior Technical Consultant to design and implement enterprise platform solutions across cloud, application, data, infrastructure, and AI workloads. This role combines strong platform engineering fundamentals with modern AI-assisted development practices. The consultant will lead cloud migration and cloud platform modernization initiatives, work directly with customers, lead technical workstreams, build reusable platform capabilities, and use AI coding tools and vibe coding techniques to accelerate solution delivery while maintaining high standards for security, quality, reliability, and governance.

Responsibilities

Lead customer workshops, technical discovery, architecture

discussions, and solution design.

Design and implement scalable platforms for cloud-native

applications, data platforms, infrastructure services, and AI

enabled workloads.

Lead cloud migration and cloud platform modernization

assessments, planning, architecture, execution, and transition

activities.

Design migration strategies, landing zones, modernization

roadmaps, workload waves, cutover plans, and operational

handoffs.

Build reusable platform patterns, automation, self-service

workflows, developer tooling, and golden paths.

Lead AI-accelerated development initiatives that integrate AI tools

into customer software development lifecycles, engineering

workflows, and delivery practices.

Drive organization-wide AI enablement for customers through

assessments, pilots, training, playbooks, governance, adoption

roadmaps, and measurement frameworks.

Use AI coding assistants and agentic development tools for

prototyping, coding, testing, debugging, refactoring, and

documentation.

Apply human review and engineering standards to validate AI

generated code and solutions.

Develop Infrastructure as Code, CI/CD pipelines, GitOps

workflows, and cloud automation.

Work with containers, Kubernetes, APIs, event-driven systems,

observability, security, and cloud-native architectures.

Understand LLM, RAG, agent, vector database, model API, and AI

application integration patterns.

Support platform security, governance, reliability, operational

readiness, and cost optimization.

Create reusable accelerators, technical documentation,

demonstrations, and reference architectures.

Mentor engineers and contribute to internal technical communities

and enablement.

Participate in proposals, estimates, technical presentations, and

pre-sales activities.

Required Qualifications

Bachelor’s degree in Computer Science, Engineering, Information

Technology, or equivalent practical experience.

8+ years of professional IT experience.

4+ years of experience in cloud, platform engineering, DevOps,

automation, application modernization, or related areas.

Hands-on experience with AWS, Azure, and/or Google Cloud.

Experience delivering cloud migration and cloud platform

modernization initiatives, including assessment, planning,

dependency analysis, landing zones, migration waves, cutover, and

validation.

Experience with Terraform, Bicep or another Infrastructure as

Code technology.

Experience with CI/CD, containers, Kubernetes, automation, and

cloud-native application patterns.

Strong scripting or programming skills in Python, PowerShell,

Bash, or a comparable language.

Practical experience using AI coding assistants or agentic

development tools.

Understanding of LLMs, generative AI applications, RAG, prompt

engineering, agents, and model APIs.

Ability to review, secure, test, and productionize AI-generated

code.

One or more relevant professional certifications, such as:

Certified Kubernetes Administrator (CKA) or Certified

Kubernetes Application Developer (CKAD).

AWS Certified Solutions Architect - Associate or Professional.

AWS Certified AI Practitioner.

Microsoft Certified: Azure AI Fundamentals or Microsoft

Certified: Azure Solutions Architect Expert.

Relevant NVIDIA AI, Generative AI, or AI infrastructure

certification.

Claude Certified Architect or an equivalent AI architecture

certification.

Strong consulting, communication, technical writing, and

customer-facing skills.

Preferred Qualifications

Experience with Amazon Bedrock, SageMaker, Azure OpenAI,

Microsoft Foundry, or Google Vertex AI.

Experience with LangChain, LangGraph, LlamaIndex, Semantic

Kernel, CrewAI, or similar frameworks.

Experience with Cursor, Windsurf, GitHub Copilot, Claude Code,

Devin, Amazon Q Developer, or similar tools.

Experience building developer platforms, platform APIs, self

service portals, or reusable engineering accelerators.

Experience with cloud migration tools such as AWS Migration Hub

or MGN, Azure Migrate, Google Migration Center, VMware HCX,

or comparable technologies.Experience with GitOps, service mesh, policy-as-code, platform

security, observability, or FinOps.

Cloud, Kubernetes, Terraform, DevOps, AI, or security

certifications.

What Success Looks like:

Customers receive secure, scalable, and reusable platform

solutions.

Customers successfully migrate and modernize workloads and

cloud platforms with clear transition and operational plans.

Engineering teams improve productivity through automation, self

service, and responsible AI-assisted development.

Platform capabilities support multiple workload types rather than

only AI workloads.

AI tools are used to accelerate delivery without compromising

quality, security, or maintainability.

The consultant builds strong customer relationships and

contributes reusable knowledge to the broader practice.