Senior Technical Consultant

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.