Solution Architect L2
Wipro · Fort Mill · $100,000–$200,000 (stated in the posting)
Wipro Limited (NYSE: WIT, BSE: 507685, NSE: WIPRO) is a leading technology services and consulting company focused on building innovative solutions that address clients’ most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, and operations, we help clients realize their boldest ambitions and build future-ready, sustainable businesses. With over 230,000 employees and business partners across 65 countries, we deliver on the promise of helping our customers, colleagues, and communities thrive in an ever-changing world. For additional information, visit us at www.wipro.com.Job Description
Key Responsibilities
• Embed within infrastructure teams and work closely with business and infrastructure leaders.
• Identify automation opportunities independently, without waiting for instructions.
• Build AI-assisted solutions using Cursor and GitHub Copilot.
• Develop infrastructure automation and build AI workflows and AI agents.
• Modernize infrastructure platforms and improve cloud migration efficiency.
• Build vulnerability remediation automation.
• Demonstrate proof-of-concepts rapidly and deliver measurable productivity improvements.
• Mentor existing engineering teams and promote AI adoption across the organization.
Required Skills & Experience
• Programming & scripting: strong Python development, scripting, and APIs.
• Infrastructure & cloud: infrastructure engineering, cloud platforms (AWS preferred), Infrastructure as Code (IaC), DevOps, and automation.
• AI-assisted engineering: GitHub Copilot, Cursor AI, prompt engineering, LLM-based software development, and AI-assisted coding.
• Ways of working: strong problem solving, a self-starter mindset, and excellent communication.
Preferred Skills
• Agentic AI workflows and AI orchestration
• Infrastructure modernization and platform engineering
• Security automation and vulnerability management
• FinOps and observability
• Cloud migration
Depth in the platforms, pipelines, and guardrails that AI-driven infrastructure runs on.
• Infrastructure as Code — Terraform, Pulumi, AWS CDK, VMware, OpenShift
• Configuration-as-Code — Ansible, Puppet
• CI/CD — GitHub Actions, ArgoCD
• Kubernetes — EKS / GKE / AKS, Helm, service mesh
• Observability — OpenTelemetry, Dynatrace, Grafana, LLM metrics
• Cloud security — VPC, PrivateLink, encryption, data residency
• Compliance — SOC 2, HIPAA, FedRAMP for LLM
• Agentic frameworks — LangGraph, CrewAI, N8N
• LLMOps — model routing (LiteLLM), semantic caching, prompt versioning, A/B testing
• Cost / FinOps for LLM
Agentic Systems FDE
Depth in designing, orchestrating, and evaluating reliable multi-agent AI systems.
• Agentic frameworks — LangGraph, CrewAI, AutoGen, Anthropic computer-use
• Tool / function calling
• Orchestration & state — retry, checkpointing, failure modes
• Multi-agent patterns
• Memory systems — short-term, long-term vector, episodic
• Human-in-the-loop approval workflows
• Non-deterministic output evaluation
• Tracing — LangSmith, Arize, OpenTelemetry
Principal FDE (future / escalation profile)
Not part of this hiring wave, but the likely escalation profile as the engagement scales.
• End-to-end enterprise AI architecture
• Provider trade-offs — Anthropic vs. Bedrock vs. Vertex vs. Azure
• AI governance and build-vs-buy decisions
• ROI / KPI ownership
• Executive presence with CTO / CDO / CFO stakeholders