AI Solution architect

Role Summary

Wipro brings a Principal AI/ML Solutions Architect to partner with your organisation in designing and delivering enterprise-scale AI, MLOps, and Generative AI platforms.

This role combines deep technical expertise with strategic advisory capability, ensuring your AI investments move beyond experimentation into production-grade, scalable, and business-impacting solutions.

What This Role Delivers

AI Strategy to Execution

• Define AI/ML and GenAI roadmaps aligned to business priorities
• Translate use cases into scalable, production-ready architectures
• Provide C-suite advisory on AI adoption, governance, and operating models

Enterprise AI Platform Engineering

• Design and implement cloud-native MLOps platforms across AWS, Azure, and GCP
• Enable end-to-end ML lifecycle: data → training → deployment → monitoring
• Build secure, automated CI/CD pipelines for model deployment at scale

Generative AI & LLM Enablement

• Deploy GenAI solutions including RAG architectures and enterprise knowledge systems
• Build agentic AI workflows for automation and decision intelligence
• Ensure responsible AI, explainability, and governance by design

Data & Real-Time Intelligence

• Implement real-time and batch data pipelines (Kafka, Spark, Airflow)
• Enable advanced feature engineering and predictive analytics
• Support streaming inference and operational AI use cases

Scaled Delivery & Transformation

• Lead multi-disciplinary AI teams across engineering, data science, and DevOps
• Deliver production-grade AI solutions in complex, regulated environments
• Accelerate outcomes using pre-built frameworks and accelerators

Core Capabilities

• AI/ML Engineering: TensorFlow, PyTorch, XGBoost, transformer architectures
• MLOps & LLMOps: MLFlow, Kubeflow, SageMaker, KServe, vector DBs
• Cloud & Platform Engineering: AWS, Azure, GCP, Kubernetes, Terraform
• Data Platforms: Spark, Kafka, Snowflake, Airflow
• Responsible AI: Explainability (SHAP), model governance frameworks

Business Impact

• Accelerated AI adoption from PoC to production
• Reduced time-to-market for ML use cases
• Improved model reliability, governance, and compliance
• Enabled scalable GenAI capabilities across the enterprise
• Delivered measurable ROI through AI-driven decisioning

Ideal Engagement Scenarios

• Enterprise AI platform modernisation
• Scaling MLOps / LLMOps capabilities
• Implementing GenAI-driven knowledge and automation platforms
• Driving AI-led transformation across business units
• use cases