Enterprise Solution Architect - AI & Cloud Platforms

Enterprise Solution Architect

Project Role : Enterprise Solution Architect
Project Role Description : Lead the development of complex solutions across multi-tower and/or multi- service transformational opportunities. Architect end-to-end integrated solutions leveraging the best mix of offerings, assets, and capabilities to maximize value for the client and Accenture. Ensure alignment of solution to client’s organization goals.
Must have skills : AI & Data Solution Architecture
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary
We're looking for a visionary Lead FullStack Engineer with 6+ years of hands-on experience in building cognitive, AI/ML-driven platforms across on-prem HCIs, private cloud, and public cloud stacks, to work on business-critical client environments. This lead-level role requires strong architectural solutioning ownership, statistical and analytical depth, and proven experience delivering end-to-end production AI systems, including time-series forecasting, recommendation engines, clustering, NLP, and deep learning solutions. The role involves technical leadership of engineering teams, direct interaction with enterprise clients, and ownership of solution design, delivery, and execution. The focus is to manage and evolve client technology landscapes, lead cross-technology migrations and modernization initiatives, handle technology crisis management, and deliver measurable business value through innovation while addressing complex technical and organizational challenges.
Roles and Responsibilities:
Lead and own end-to-end AI/ML solution delivery, covering problem formulation, statistical modelling, feature engineering, model development, evaluation, production deployment, and post-deployment monitoring, with full lifecycle accountability.
-Apply strong statistical foundations (probability, hypothesis testing, regression, time-series analysis) to design robust, explainable, and reliable ML systems, and review technical approaches proposed by team members.
-Design and govern knowledge-driven architectures, including knowledge graphs, entity relationship modelling, semantic layers, and decision intelligence frameworks, ensuring scalability and maintainability.
-Lead the development and productionization of time-series forecasting systems, recommendation engines, clustering and segmentation models, anomaly detection pipelines, NLP, and deep learning solutions for enterprise-scale use cases.
-Establish and oversee MLOps practices, including CI/CD for ML, model versioning, monitoring, drift detection, retraining strategies, scalable inference, governance, and Responsible AI controls.
-Drive architectural solutioning and technical decision-making, translating complex business and data problems into scalable, resilient, cloud-native system designs, and act as the final technical authority for the solution.
-Engage directly with clients and stakeholders, gather requirements, present technical solutions, lead architecture and design discussions, and act as a trusted technical advisor.
-Design, develop, and maintain end-to-end cognitive HCIs and private cloud platforms, integrating intelligence into traditional web stacks.
-Lead the development and management of full-stack infrastructure platforms, including backend services (APIs, microservices) and API gateways for frontend and backend services.
-Provide technical leadership for GPU-based computing and High Performance Computing (HPC) environments supporting AI/ML workloads.
-Drive and review implementations involving AWS Outposts, Azure Stack, and Google Cloud VPC-based hybrid architectures.
-Lead Tanzu and Red Hat OpenShift cluster deployments on private cloud, including design, deployment, and ongoing operations.
-Architect and develop cloud-native backend services using Node.js, Python (FastAPI/Flask), or Java to integrate AI models with application logic.
-Oversee the integration of AI/ML models (TensorFlow, PyTorch, scikit-learn) into secure, scalable, production-grade APIs and microservices.
-Ensure code quality, performance, and maintainability, and manage seamless integration between front-end interfaces and backend infrastructure services.
-Collaborate with product, design, ML, DevOps, and security teams to deliver intelligent workflows and high-quality user experiences.
-Lead the implementation of Infrastructure as Code (IaC) using Terraform, CloudFormation, Azure DevOps, Pulumi, or GCP-native tools.
-Oversee the deployment and lifecycle management of Platform-as-a-Service (PaaS) offerings.
-Design and govern database architectures, including relational (MySQL, PostgreSQL, SQL Server) and NoSQL (MongoDB, DynamoDB) systems.
-Ensure platform observability and operational excellence via metrics, logging, and monitoring frameworks (Prometheus, ELK, CloudWatch).
-Lead containerization and orchestration strategies using Docker and Kubernetes.
-Ensure adherence to security best practices, compliance requirements, and organizational policies across platforms and AI systems.
-Continuously evaluate emerging cloud, AI, and platform technologies and drive their adoption to improve efficiency and client outcomes.
-Act as an escalation point during production incidents and technology crises, providing leadership in root cause analysis and resolution.
-Integrate and govern AI-driven tools and frameworks, including Generative AI and Agentic AI technologies, within enterprise cloud platforms and applications.

Telecom domain expertise:
- Strong understanding of mobile network technologies, including 2G, 3G, 4G, and 5G architectures, RAN protocols, and radio propagation principles.
- Mandatory expertise in radio network planning and design concepts and methodologies.
- Hands on experience with OSS platforms from major OEMs such as Ericsson, Nokia, and Huawei, with expert level knowledge of RAN/Core OSS KPIs and practical exposure to KPI optimization.
- Experience in customer experience management within telecom networks, including understanding of multiple data sources such as crowd-sourced data, drive test data, probe data, network traces, and strong capability to correlate these datasets for insights.
- Knowledge of emerging telecom technologies, including Open RAN architecture, RIC (Near RT/Non RT), R Apps, xApps, and understanding of their integration and impact on legacy network environments.
- Strong understanding of OEM-specific radio network parameters, counters, and KPIs, along with knowledge of radio features and the latest 3GPP releases.
- Experience with SON (Self Organizing Networks) modules from any Tier 1 OEM, including configuration, operation, and optimization of SON functionalities.
Professional and Technical Skills:
Systems Operatioms and Engineering RHEL and SUSE and OLE and/or Wimdows/Hperv and/or AIX/HPUX/Soalris
Hyper Converged Infrastructure and Private Cloud Vmware/ Hyper V/ KVM//Pacemaker / AHV /OpenStack / Scale Computing / Nvidia Omniverse
Infrastructure as a Code & Scripting Terraform or Ansible - Shell Scripting and Python, Pwer Shell, Power CLi
Public Cloud IaaS and Associated PaaS AWS / Azure/ GCP - S3, Blobs, VPCs, vNet, LBs Cloud Watch, Stack Driver, Azure Mon
Cloud Native and Containers PODMAN, Docker Openshift and AKS and GKE and EKE
Database Systems (RDBMS, No SQl and Cloud DB) Orcle and MS SQL or MySQL and Mongo or Couch - Cloud DB (Redis or Aurora, Sql online
Midleware (WebServers, Message Qs, Managed Apps, MFT, Job Schedulers IIS, Apache, JBOSS, WebSphere/ Web Logic MFTs, MQ Series, Control M or Autosys or TWS and MTFs
Observability & Environment Health observability tools (Nagios /SolarWinds/Netcool, Prometheus, ELK) and Environment Health and Capacity Management and Tech Debt and FinOps
Enterprise AI Agentic AI Framework (CrewAI, LangGraph, AutoGen) and Responsible AI Concepts and AI Guardrails
Additional Information :
- Systems Operatioms and Engineering Professional
- VMware Certified Cloud Expert -the VMware Certified Professional VMware Cloud (VCP VMC) 2022
- RHCE ( Red Hat Certified Engineer)
- Nvdia Certified Engineer / Nvidia Certified Associate
- Microsoft Certified: Azure Solutions Architect Expert
- Google Professional Cloud Architect /Machine Learning
- Certified Kubernetes Administrator (CKA)
- HashiCorp Certified: Terraform Associate
- Certified DevOps Engineer certifications (AWS, Azure, or Google)
- AI Powered Tech Talent