Job Title : AI Architect

Location : Bangalore

EXP : 12+Years

Description

– AI Architect (Agentic AI / GenAI)

Position

AI Architect – Agentic AI & Generative AI

Experience: 12+ Years

Location: Flexible

Industry: Healthcare (Preferred) , Enterprise Digital Transformation

Role Overview

We are seeking an experienced AI Architect with 12+ years of overall IT experience and strong hands-on expertise in designing, building, and scaling Agentic AI solutions. The ideal candidate should possess deep technical knowledge of Multi-Agent Systems, Agentic AI Frameworks, MCP (Model Context Protocol), Retrieval-Augmented Generation (RAG), LLM orchestration, and enterprise AI architectures.

This role requires a blend of architecture leadership, hands-on development capability, AI strategy, and customer-facing consulting experience. The candidate will lead the design of reusable AI platforms, agent ecosystems, and enterprise-grade AI solutions while ensuring scalability, security, governance, and responsible AI practices. Concepts such as multi-agent orchestration, RAG architectures, MCP-based integrations, and AI governance are recurring themes in the organisation's AI initiatives.

Key Responsibilities

Solution Architecture & Design

Design end-to-end Agentic AI architectures for enterprise-scale business problems.

Architect multi-agent ecosystems with orchestration, memory, planning, reasoning, and execution capabilities.

Define AI platform blueprints, reusable agent frameworks, governance models, and deployment patterns.

Design scalable AI solutions using cloud-native architectures on Azure, AWS, or GCP.

Drive architecture reviews, technical governance, and AI best practices.

Agentic AI Development

Build and deploy autonomous AI agents and multi-agent systems.

Design agent orchestration workflows using frameworks such as:

LangGraph

CrewAI

AutoGen

LangChain

Semantic Kernel

Implement agent planning, tool usage, memory management, and human-in-the-loop controls.

MCP & Integration Architecture

Design and implement MCP (Model Context Protocol) servers and connectors.





Build reusable MCP services for enterprise applications, knowledge repositories, APIs, databases, SharePoint, healthcare systems, and third-party platforms.

Define enterprise integration patterns for AI agents and enterprise applications. MCP-enabled architectures and reusable agent ecosystems are being discussed as a strategic direction for healthcare AI engagements.

RAG & Knowledge Systems

Architect enterprise-grade RAG platforms.

Design vector databases, embedding pipelines, knowledge indexing strategies, and semantic search solutions.

Implement hybrid search, document intelligence, knowledge graphs, and contextual retrieval techniques.

Optimize retrieval quality, hallucination mitigation, evaluation frameworks, and response grounding. RAG and semantic-searcharchitectures are a core area of current internal AI learning and solution development.

AI Governance & Responsible AI

Define AI observability, security, compliance, monitoring, and guardrail frameworks.

Establish prompt governance, agent governance, LLMOps, and AI lifecycle management.

Ensure compliance with enterprise security and privacy standards.

Lead AI risk management and model evaluation initiatives.

Stakeholder Engagement

Collaborate with business leaders, product owners, architects, and engineering teams.

Translate business requirements into scalable AI solutions.

Conduct technical workshops, solution reviews, and executive presentations.

Mentor architects, engineers, and AI practitioners.

Required Technical Skills

Must Have

12+ years of software engineering and architecture experience.

5+ years in AI/ML, GenAI, or Intelligent Automation solution design.

Hands-on experience building and deploying Agentic AI applications.

Solid expertise in

Multi-Agent Systems





Agent Orchestration

AI Planning & Reasoning

LLM-based architectures

Hands-on experience with:

MCP (Model Context Protocol)

RAG Architectures

Vector Databases

Knowledge Retrieval Systems

Expertise in Python and modern AI development stacks.

Experience with OpenAI, Anthropic Claude, Gemini, Llama, Mistral, or similar models.

Strong understanding of

Prompt Engineering

Embeddings

Semantic Search

Fine-tuning approaches

Evaluation frameworks

Experience working with Azure AI, AWS Bedrock, Azure OpenAI, or equivalent platforms.

Knowledge of CI/CD, DevOps, MLOps, and LLMOps.

Preferred Skills

Experience with

LangGraph

CrewAI

AutoGen

Semantic Kernel

Experience implementing AI governance, Responsible AI, and AgentOps frameworks

Experience integrating AI solutions with enterprise applications, APIs, and knowledge repositories.

Healthcare Domain Experience (Preferred)

Candidates with healthcare experience will be strongly preferred.

Desired exposure

Payer/Provider systems

Care Management

Utilization Management

Claims Processing

Prior Authorization

Member Engagement

Clinical Documentation

Healthcare AI Assistants

HIPAA-compliant AI solutions

Healthcare knowledge management and decision-support systems

Experience delivering AI/GenAI use cases in healthcare, insurance, or regulated industries would be a significant advantage. Healthcare-focused AI automation, agent ecosystems, and reusable architecture patterns are active areas of organisational interest.

Educational Qualifications

Master's degree ( Phd in AI/ML preferred) in Computer Science, Engineering, AI, Data Science, or related discipline.

Relevant AI/Cloud certifications preferred.

Success Profile The ideal candidate is:

A hands-on technical leader.

Comfortable coding, prototyping, and architecture design.

Experienced in building production-grade Agentic AI systems.

Capable of defining enterprise AI strategy and reusable platforms.

Able to engage confidently with clients, CTOs, architects, and leadership teams.

📌 AI Architect (Pune)
🏢 NR Consulting
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