FDE_Sr Technical Architect

City: Bengaluru

State/Province: Karnataka

Posting Start Date: 4/17/26

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.

• Experience building GenAI applications, including RAG, multi-agent systems, fine-tuning, etc., with tools such as LangChain, LangGraph etc.
• Clear understanding of Model Context Protocols, A2A Protocols, Agent Developeer Kit and working experience with LLMs
• Expertise in deploying production grade GenAI solutions, including evaluation and optimizations; Machine Learning deployments on AWS, Azure or GCP
• Extensive hands-on data science experience, leveraging machine learning and data science tools (i.e., pandas, scikit-learn, PyTorch, etc.)
• Experience with DevOps tools: Kubernetes, Docker, Terraform, CI/CD Pipelines, GitHub/GitLab, GitOps, GitHub Actions, Jira, Jenkins, CircleCI, Datadog, Slack.
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• 15 years of engineering and technical deployment experience in a customer-facing set up
• Should scoped and delivered complex systems in rapid and ambiguous environments
• Delivered production-grade code across frontend and backend using Python, JavaScript, or similar stacks
• Understand how AI model behaviour affects product experience
• Communicate clearly with engineers, product teams, and customer stakeholders
• Flag risks early and seek attention as per the severity.
  Key responsibilities:
  
  • Work with go-to-market teams to win clients for AI platform / agent offerings
  • Help clients integrate and adopt the offerings; demonstrate the impact / outcomes the offerings commited such as KPI improvements and help client succeed
  • Embed within the client landscape, understand their domain and co-develop solutions with the core product engineering teams
  • Own technical delivery across multiple deployments from prototype to stable release
  • Drive product roadmap and make decisions on scope and priorities
  • Develop deployment architectures for cloud or on-prem installation
  • Systemize working patterns into tools, playbooks, or reference frameworks/templates for sharing across client and product engineering teams
  Implement and administer best practices

• 15 years overall; 3-5 years architecting AI/ML/GenAI solutions at scale.
• Hands-on with LLMs (GPT family, BERT/Transformers), prompt engineering, LLM optimization (context windows, adapters), RAG, and evaluation (golden sets, BLEU/BERTScore/semantic similarity).
• Cloud: Azure (incl. Azure AI, OpenAI, Cognitive Services), AWS (Bedrock/SageMaker), GCP (Vertex AI); containerization (Docker/Kubernetes) and service meshes.
• Data & Integration: SQL, NoSQL, data warehousing concepts (fact/dimension, SCD), ETL/ELT, streaming (Kafka/Event Hubs), API design, and secure integration (OAuth2/JWT/RBAC).
• LLMOps/MLOps & Automation: CI/CD for models, model registry, feature store, monitoring (tokens, drift, hallucinations), Python automation for infra and pipelines.

• Proficiency in Python, LangChain/LangGraph, vector DBs, and prompt/agent frameworks; strong Git, CI/CD, IaC, and Agile/Scrum delivery.

• Prior ownership of complex AI solution designs, HLD/LLD, NFRs, and security/ compliance patterns; ability to mentor squads and lead design forums.

• Experience designing AI gateways, MCP (Model Context Protocol) implementations, and agent registries; familiarity with metadata standards (DCAT/JSON-LD).
• Exposure to enterprise AI platforms/labs and accelerators
• Knowledge of infrastructure automation in DC/cloud (orchestration, provisioning) with AI workloads and GPU scheduling (K8s, NCCL, Triton).
• Prior work on Microsoft Copilot Studio solutions, conversational agents, and Power Platform extensibility.

• B.Tech or Equivalent degree
• Certifications (any subset): Azure AI Engineer/Architect, Azure OpenAI, AWS/GCP AI, Databricks ML, Kubernetes