AI Architect AI ARCHITECT | JOB DESCRIPTION Data Science, Machine Learning, Generative AI & Agentic AI Role AI Architect - DS, ML, GenAI & Agentic AI Experience 10-15+ years overall; 5+ years in AI/ML Preferred Platform Role Summary Microsoft Azure AI Foundry We are looking for an experienced AI Architect to design and deliver enterprise-scale AI solutions across Data Science, Machine Learning, Deep Learning, Natural Language Processing, Generative AI and Agentic AI.

The ideal candidate combines robust AI fundamentals with hands-on architecture and engineering experience, particularly in building AI agents and multi-agent solutions using Microsoft Azure AI Foundry.

The role owns the journey from business problem definition through architecture, prototyping, production deployment, governance and operationalization.

Key Responsibilities 1. AI/ML Architecture Design scalable enterprise AI/ML architectures covering data ingestion, feature engineering, training, inference, deployment and monitoring. Architect solutions across supervised and unsupervised learning, classification, regression, clustering, forecasting and predictive analytics. Define model selection, validation, evaluation and optimization strategies. Design reusable AI platforms, frameworks and accelerators. 2.

Data Science, ML & Deep Learning Apply statistical modeling, experimentation, feature engineering and model interpretability techniques. Guide implementation of regression, decision trees, Random Forest, XGBoost, clustering, dimensionality reduction, forecasting, neural networks, CNNs, RNN/LSTM and Transformers. Provide hands-on technical direction using Python, pandas, Num

Py, scikit-learn, PyTorch and/or Tensor

Flow. Define production ML lifecycle and MLOps practices. 3. NLP & Generative AI Design NLP solutions for classification, information extraction, summarization, semantic search and conversational AI. Architect enterprise Retrieval-Augmented Generation (RAG) solutions using embeddings, vector search, chunking, reranking and grounding. Define prompt engineering, structured output, tool-calling, model evaluation and guardrail patterns. Select the right approach across prompt engineering, RAG, fine-tuning and traditional ML based on accuracy, cost, latency and risk. 4.

Agentic AI Architecture Architect enterprise AI agents and multi-agent systems incorporating planning, reasoning, tool calling, knowledge retrieval, workflow execution and human-in-the-loop controls. Define agent orchestration, state management, memory, context management, exception handling and recovery patterns.





Confidential | Talent Acquisition | Page 1 Create deterministic controls around non-deterministic LLM behavior. AI ARCHITECT | JOB DESCRIPTION Implement Agent

Ops practices including tracing, observability, evaluation, security, governance and cost monitoring. 5.

Microsoft Azure AI Foundry Build and configure AI agents using Microsoft Azure AI Foundry. Lead model selection, deployment, prompt orchestration, tool/function integration, enterprise knowledge grounding, evaluation, tracing, safety and guardrails. Integrate Foundry solutions with Azure OpenAI, Azure AI Search, Azure Functions, Azure API Management, Azure Key Vault, Azure Storage, Microsoft Fabric and Databricks. Apply identity, access control, network security, monitoring and production deployment practices. 6.

Enterprise AI Architecture Translate business use cases into scalable, secure and cost-effective AI architectures. Define reference architectures spanning channels, agent layer, orchestration, models, knowledge/RAG, enterprise APIs and data platforms. Integrate agents with enterprise applications, APIs, databases and knowledge repositories using API-first and event-driven patterns. Design for scalability, latency, availability, resiliency, maintainability and cloud cost optimization. 7. AI Governance & Responsible AI Establish principles for Responsible AI, model and agent governance, data privacy, PII protection, prompt injection defense, hallucination mitigation, content safety, access control and auditability. Define evaluation frameworks covering accuracy, groundedness, relevance, safety, latency, reliability and cost. Partner with security, legal, risk and compliance teams to operationalize enterprise controls.

Technical Skills Area Expected Skills Programming Python, SQL Data Science Machine Learning Deep Learning pandas, Num

Py, scikit-learn, statistics, experimentation Regression, classification, clustering, ensembles, forecasting NLP PyTorch/Tensor

Flow, neural networks, Transformers Embeddings, semantic search, information extraction, conversational AI Generative AI Agentic AI LLMs, RAG, prompt engineering, evaluation, guardrails Agents, multi-agent systems, tool calling, planning, memory, orchestration Microsoft AI Agent Frameworks Azure AI Foundry, Azure OpenAI,



Azure AI Search Azure AI Foundry Agent capabilities, Semantic Kernel and/or Lang

Graph Data Platforms MLOps/LLMOps Databricks, Microsoft Fabric, Data Lake CI/CD, model deployment, monitoring, evaluation, versioning, Agent

Ops Cloud & Dev

Ops Architecture Microsoft Azure, Git, Azure Dev

Ops/Git

Hub, Docker, Kubernetes Microservices, APIs, event-driven architecture, cloud-native design Mandatory Skills Strong Data Science, Machine Learning, Deep Learning and NLP fundamentals. Hands-on Generative AI, LLM and enterprise RAG implementation experience. Agentic AI architecture and implementation experience.

Confidential | Talent Acquisition | Page 2 Hands-on AI agent development using Microsoft Azure AI Foundry. AI ARCHITECT | JOB DESCRIPTION Experience integrating agents with enterprise APIs, databases, applications and knowledge repositories. Strong Python development and solution engineering capability. Understanding of MLOps, LLMOps, Agent

Ops, security and Responsible AI. Proven ability to move beyond proof-of-concepts and deliver production-grade enterprise AI solutions.

Preferred Skills Experience with Semantic Kernel and/or Lang

Graph. Knowledge of Knowledge Graphs, GraphRAG and ontology-driven AI. Experience with Databricks or Microsoft Fabric. Multi-agent orchestration and Model Context Protocol (MCP) concepts. Experience building reusable enterprise AI platforms, AI factories or workbenches. Experience with AI observability, evaluation and red-teaming frameworks. Azure architecture or AI certifications. Experience leading AI architecture for large enterprise transformation programs.

Leadership & Collaboration Lead architecture workshops, design reviews and technical decision-making with business and technology stakeholders. Guide engineering teams through implementation, code reviews, troubleshooting and production readiness. Communicate complex AI concepts clearly to senior executives, product owners, engineers, data scientists, security teams and clients. Mentor architects, data scientists and AI engineers while establishing reusable standards and best practices.

Ideal Candidate Profile The successful candidate is not purely conceptual.

They can whiteboard an enterprise AI architecture and go deep into implementation across Python, ML/DL, Transformers, embeddings, RAG, LLMs, agents, Azure AI Foundry, enterprise integration, security, evaluation and production operations.

They can guide engineering teams, review architecture and code, resolve complex design issues and engage confidently with senior business and technology stakeholders.

📌 AI Architect - Wipro (India)
🏢 TRIGENT SOFTWARE PRIVATE
📍 India