AI Solution Architect

About the job

Role Overview

We are looking for an experienced AI Solution Architect. The candidate should have strong understanding of Data Science, AI/ML, Generative AI, Agentic AI, cloud architecture, enterprise integrations, and AI systems development. The role requires someone who can understand business problems, convert them into practical AI solutions, design end-to-end architectures, and engage confidently with business and technical stakeholders.

Key Responsibilities

• Lead solutioning for ML, Generative AI, Agentic AI, RAG, and AI automation use cases.
• Design end-to-end AI architectures covering data sources, data pipelines, AI models, APIs, integrations, application components, security, governance, monitoring, and deployment.
• Collaborate with AI, Cloud, Software Engineering, Integration, and Delivery teams to ensure proposed solutions are practical and implementable.
• Guide delivery teams during project initiation, design, build, testing, deployment, and handover.

Required Skills

• 8+ years of experience in technology, Data Science and AI.
• Practical experience in Data Science and AI systems development, including data preparation, feature engineering, model development, model evaluation, APIs, deployment, and production monitoring.
• Strong understanding of AI/ML use cases such as churn, segmentation, forecasting, recommendation, fraud detection, anomaly detection, risk scoring, and optimization.
• Good understanding of GenAI concepts including LLMs, prompt engineering, embeddings, vector search, RAG, agents, tool calling, guardrails, evaluation, and LLMOps.
• Experience in enterprise solution architecture across application, data, integration, infrastructure, security, and monitoring layers.
• Ability to prepare technical proposals, RFP responses, architecture diagrams, scope documents, and effort estimates.
• Strong communication and consulting skills with the ability to engage business, technical, and executive stakeholders.

Preferred Skills

• Experience with Azure AI services such as Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure ML, Document Intelligence, API Management, Cosmos DB, Redis, and Azure Monitor.
• Exposure to MLOps / LLMOps tools such as MLflow, Kubeflow, Databricks, Azure ML, or OpenShift AI.
• Experience with vector databases such as Azure AI Search, Qdrant, Milvus, Pinecone, or FAISS.
• Experience developing AI-enabled applications, APIs, backend services, workflow automation, or production-grade AI components.
• Experience in banking, telecom, government, or enterprise AI use cases.