Cloud Platform Architect

Cloud Platform Architect

Project Role : Cloud Platform Architect
Project Role Description : Oversee application architecture and deployment in cloud platform environments -- including public cloud, private cloud and hybrid cloud. This can include cloud adoption plans, cloud application design, and cloud management and monitoring.
Must have skills : Cloud Design and Build
Good to have skills : Cloud Strategy and Assessment, Cloud Automation DevOps, Docker Kubernetes Architecture, Infrastructure As Code (IaC), Database Architecture
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
This is not a consulting role or a project delivery role. A Forward Deployed Infrastructure Transformation Engineer is a production engineer who works embedded inside a client's enterprise, shoulder to shoulder with their teams, to transform legacy infrastructure & Application estates into AI-ready, cloud/Hybrid platforms and Modernized Infrastructure. You own outcomes: time-to-value, reliability, adoption, and scalability. Not delivery milestones. Outcomes.
The Role expects the expertise to build cloud including Data and AI foundations that is secure & scalable and responsive — designing AI Infrastructure architectures including Compute, Data, Networking and security aspects leveraging Hybrid cloud models.
Accenture's Forward Deployed Infrastructure Transformation Engineers operate at the intersection of engineering depth and business reinvention. This is where the most complex enterprise infrastructure challenges get solved — not in a lab, but inside real client environments, with real constraints, at real scale.

Roles & Responsibilities:
1) Embed directly with client infrastructure and engineering teams to design, execute, and operationalize end-to-end transformation — spanning cloud foundation, hyperconverged infrastructure, platform engineering, application modernization, database transformation, SAP, network and security, and AI platform deployment — inside real enterprise environments
2) Accountable for production outcomes — platform reliability, time-to-value, adoption, and scalability — measured against business metrics, not project milestones.
3) Move from ambiguous infrastructure problems to working production environment through rapid iteration: days to validated architecture, weeks to production-ready deployment.
4) Execute application migration hands-on — rehost (lift & shift), re-platform (lift & optimize), and redeploy — applying the right strategy per workload to land on an AI-ready, modernized target platform.
5) Architect and implement network and security foundations: Zero Trust, SASE/SSE, micro-segmentation, container security, and application AI governance across enterprise environments.
6) Design and govern transformation architectures across the full enterprise stack: compute (HCI/cloud), platform engineering and Internal Developer Platforms (IDPs), Kubernetes orchestration, CI/CD pipelines, network, security, storage, containers, databases, applications, and AI platform layers.
7) Build the Data and AI foundation layer: design data architectures for structured and unstructured data deploy the AI platform layer that enables modernized workloads to leverage AI at scale.
8) Architect and modernize database estates — relational and NoSQL database migration, schema conversion, performance tuning, and cloud-native database deployment — as the data layer underpinning the modernized platform.
9) Leverage Python end-to-end — infrastructure automation, deployment scripting, data pipeline engineering, and AI agent development across the full transformation lifecycle.
10) Translate technical architecture into business impact for client CTO, CFO, and CISO shape modernization roadmaps, ROI backlogs, and AI adoption strategy.
11) Build reusable patterns, playbooks, and accelerators through architecture workshops, proofs of concept, and code-with sessions — leaving the client team fully capable of operating, scaling, and extending the platform independently, and codifying learnings that grow the FDE practice
Professional & Technical Skills:
1) Engineering Mindset should come in the beginning.
2) Minimum 2 years of working experience of AI platform ecosystems — cloud-native AI services (AWS, Azure, GCP), open-source model deployment, and the infrastructure layer required to run AI workloads reliably at enterprise scale.
3) 3 to 5 years of engineering experience with cloud-native systems. & have the ability to deploy and guiding team to deploy AI use cases
4) Minimum 1 years of experience designing, deploying and operating AI platforms and infrastructure — model serving environments, AI-ready compute, data pipelines, and platform integration — in production enterprise environments
5) 10+ years of infrastructure engineering experience, with demonstrable production delivery across cloud platforms, network, Security, Kubernetes, containerization, platform engineering, and enterprise application environments.
6) Track record of owning and delivering infrastructure transformation outcomes inside live enterprise environments — not advisory engagements, internal initiatives, vendor labs, or contained team deployments.
7) Ability to connect infrastructure decisions to business/financial outcomes — translating platform reliability, migration efficiency, and modernization gains into business impact a CFO would fund and act on.
8) Experience presenting to and building trust with senior client stakeholders at CTO, CFO, or CISO level.
9) Non-linear profiles are expected and welcomed — assessment is based on demonstrated delivery experience and outcome ownership, not CV pattern matching.

Additional Information:
1) Strong problem-solving skills
2) Flexible to work in 24*7 environment