AI Solution Architect

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.