Position: AI Architect
Experience: 12–17 Years
Job Location: PAN India
Work Mode: Hybrid / Work From Office (WFO)
Shift: General Shift
Joining Preference: Immediate Joiners to 60 Days Notice Period
Education: BE / B.Tech / M.Tech / MCA
Role Type: Full-Time
About the Role
We are looking for a highly experienced AI Architect with 12–17 years of overall experience to lead the architecture, design, and implementation of scalable, secure, and enterprise-grade AI solutions.
The ideal candidate will have strong expertise in AI/ML architecture, Generative AI, Large Language Models (LLMs), RAG, Vector Databases, Agentic AI frameworks, Enterprise AI Platforms, Responsible AI, and Cloud AI Architecture.
The candidate will work closely with technology leadership, engineering teams, data scientists, cloud teams, product stakeholders, and enterprise architects to define AI strategies and translate business requirements into robust technical architectures.
This role requires a combination of deep technical expertise, enterprise architecture capability, hands-on understanding of modern GenAI technologies, and strong stakeholder management skills.
Key Responsibilities
1. AI & Enterprise Architecture
- Define and own end-to-end AI/GenAI architecture for enterprise-scale applications.
- Design scalable, resilient, secure, and high-performance AI solutions aligned with enterprise architecture standards.
- Translate business requirements and use cases into AI solution architectures, technical blueprints, and implementation roadmaps.
- Define architecture patterns for LLM-based applications, AI agents, knowledge systems, recommendation systems, and intelligent automation.
- Establish reusable AI architecture patterns, frameworks, components, and reference architectures.
- Evaluate build-vs-buy decisions for AI platforms, models, frameworks, and infrastructure.
- Drive architectural decisions across application, data, AI/ML, integration, security, and cloud layers.
- Ensure AI solutions are designed for scalability, maintainability, observability, reliability, and cost efficiency.
2. Generative AI & LLM Architecture
- Architect enterprise-grade Generative AI and LLM solutions using commercial and open-source models.
- Design LLM-powered applications including:
- AI assistants and copilots
- Enterprise chatbots
- Knowledge management systems
- Intelligent document processing
- AI-powered search
- Summarization and content generation
- Agentic AI applications
- Workflow automation
- Evaluate and select appropriate LLMs based on performance, cost, latency, security, and business requirements.
- Design strategies for model selection, model routing, prompt engineering, fine-tuning, grounding, evaluation, and monitoring.
- Define architecture for multi-model and model-agnostic enterprise AI platforms.
3. LangGraph
- Design and implement agentic AI architectures using LangGraph.
- Develop stateful, multi-step, and multi-agent workflows.
- Design orchestration patterns for AI agents, tools, memory, workflows, and human-in-the-loop interactions.
- Establish reusable LangGraph architecture patterns for enterprise use cases.
- Integrate LangGraph-based solutions with enterprise APIs, databases, vector stores, security frameworks, and cloud services.
- Define approaches for agent observability, error handling, state management, guardrails, and production deployment.
4. Microsoft Semantic Kernel
- Architect AI applications and agentic workflows using Microsoft Semantic Kernel.
- Design integrations between LLMs, enterprise applications, plugins, APIs, memory stores, and business workflows.
- Establish reusable Semantic Kernel components and enterprise integration patterns.
- Evaluate and select between frameworks such as LangGraph and Semantic Kernel based on use-case and enterprise requirements.
- Drive adoption of enterprise-grade orchestration frameworks for GenAI applications.
5. RAG & Knowledge Architecture
- Design scalable Retrieval-Augmented Generation (RAG) architectures.
- Define document ingestion, parsing, chunking, embedding, indexing, retrieval, reranking, and response-generation strategies.
- Architect advanced RAG patterns such as:
- Hybrid search
- Semantic search
- Metadata filtering
- Multi-stage retrieval
- Query rewriting
- Reranking
- Multi-document RAG
- Graph RAG
- Agentic RAG
- Optimize RAG systems for accuracy, latency, scalability, and cost.
- Define strategies for reducing hallucinations through grounding, citations, validation, and contextual retrieval.
6. Vector Databases
- Design and implement enterprise-grade Vector Database architectures.
- Evaluate and work with technologies such as:
- Azure AI Search
- Pinecone
- Weaviate
- Milvus
- OpenSearch
- pgvector
- Other enterprise vector stores
- Define embedding strategies, indexing approaches, similarity search, metadata filtering, and data lifecycle management.
- Design vector infrastructure for high availability, scalability, performance, and security.
- Establish appropriate vector database architecture based on data volume, query patterns, latency, and enterprise requirements.
7. Enterprise AI Platform
- Define architecture and roadmap for an Enterprise AI Platform supporting multiple business units and AI use cases.
- Design reusable platform capabilities including:
- Model gateway
- Prompt management
- RAG services
- Agent orchestration
- Vector search
- AI evaluation
- Guardrails
- AI observability
- Model monitoring
- Cost management
- Security and access control
- Enable centralized governance while allowing development teams to rapidly build and deploy AI applications.
- Define standards for AI application development, deployment, monitoring, and lifecycle management.
- Drive platform standardization and reusable AI services across the organization.
8. Cloud AI Architecture
- Design and implement cloud-native AI architectures across major cloud platforms.
- Strong understanding of cloud AI services, infrastructure, networking, security, storage, compute, and managed AI/ML services.
- Experience with one or more of:
- Microsoft Azure
- AWS
- Google Cloud Platform (GCP)
- Design secure and scalable cloud architectures for LLM and GenAI workloads.
- Define architecture for AI workloads involving GPUs, model hosting, APIs, data platforms, vector databases, and enterprise applications.
- Optimize AI infrastructure for performance, availability, scalability, and cloud cost.
- Ensure compliance with enterprise cloud security and governance standards.
9. Responsible AI & AI Governance
- Establish and implement Responsible AI principles across AI solutions.
- Define architecture controls for:
- AI safety
- Privacy
- Security
- Bias and fairness
- Transparency
- Explainability
- Auditability
- Data governance
- Model governance
- Design guardrails to prevent inappropriate, unsafe, or unauthorized AI responses.
- Establish mechanisms for prompt/response monitoring, content filtering, PII protection, and human oversight.
- Define AI risk assessment and governance frameworks for enterprise deployments.
- Ensure AI implementations comply with organizational policies and applicable regulatory requirements.
10. Architecture Governance & Leadership
- Lead architecture discussions with senior technology and business stakeholders.
- Conduct architecture reviews and provide technical recommendations.
- Create and maintain:
- High-Level Design (HLD)
- Low-Level Design (LLD)
- Architecture Decision Records (ADRs)
- Reference architectures
- Technical standards
- Technology evaluation documents
- Mentor senior engineers, architects, and AI development teams.
- Lead technical PoCs and innovation initiatives.
- Stay current with developments in GenAI, Agentic AI, LLMs, AI platforms, cloud AI, and AI governance.
- Present architecture proposals and technical strategies to leadership and enterprise architecture teams.
Key Skills & Technical Expertise
Mandatory Skills
- AI / GenAI Architecture
- Enterprise Architecture
- Generative AI / LLMs
- LangGraph
- Microsoft Semantic Kernel
- RAG
- Vector Databases
- Enterprise AI Platform Architecture
- Responsible AI
- Cloud AI Architecture
- AI/LLM orchestration
- AI Agents / Agentic AI
- Prompt Engineering
- Embeddings
- Semantic Search
- AI Security and Governance
Cloud & Platform Skills
Strong experience in at least one major cloud platform:
- Microsoft Azure
- AWS
- Google Cloud Platform
Knowledge of:
- Cloud-native architecture
- API architecture
- Microservices
- Containers / Kubernetes
- CI/CD
- Infrastructure automation
- Cloud security
- Observability and monitoring
AI/ML & Data Skills
- Machine Learning fundamentals
- Deep Learning fundamentals
- NLP
- LLM architecture and application patterns
- Embedding models
- Vector search
- Knowledge bases
- Data pipelines
- Model evaluation
- AI observability
- Model monitoring
- MLOps / LLMOps
Preferred Additional Skills
- Python
- REST APIs
- Microservices architecture
- Docker
- Kubernetes
- API gateways
- SQL / NoSQL databases
- Event-driven architecture
- Data engineering concepts
- Knowledge Graphs
- Graph RAG
- Multi-agent systems
- AI evaluation frameworks
- LLM security
- Prompt security
- AI red teaming
- Azure OpenAI / AWS Bedrock / Google Vertex AI
- Enterprise IAM and security architecture
Experience Requirements
- 12–17 years of overall IT experience.
- Significant experience in solution/technical architecture.
- Strong hands-on experience with Generative AI and LLM-based solutions.
- Proven experience designing enterprise-scale AI platforms and applications.
- Experience leading architecture decisions and technical teams.
- Strong experience working with senior stakeholders and enterprise technology teams.
- Demonstrated experience taking AI solutions from PoC to production.
- Experience with cloud-based AI deployments and enterprise security requirements.
Educational Qualification
Mandatory:
- BE / B.Tech in Computer Science, Information Technology, Electronics, or related discipline
OR - M.Tech / MCA or equivalent qualification.
Additional certifications in Cloud, AI/ML, Generative AI, Solution Architecture, or related technologies would be an advantage.
Soft Skills & Leadership Competencies
- Strong architectural and analytical thinking.
- Excellent communication and presentation skills.
- Ability to explain complex AI concepts to both technical and non-technical stakeholders.
- Strong problem-solving and decision-making capabilities.
- Ability to influence technology strategy and architecture decisions.
- Strong stakeholder management skills.
- Ability to mentor and guide engineering teams.
- Comfortable working in a fast-paced enterprise environment.
- Strong ownership and accountability.
- Ability to balance innovation with enterprise security, governance, and operational requirements.
Location & Work Model
Location: PAN India
Work Model: Hybrid / Work From Office (WFO)
Candidates should be comfortable working from the organization's designated office location based on business requirements.
Shift: General Shift
Notice Period
Preferred: Immediate Joiners to 60 Days
Candidates serving notice periods of up to 60 days may be considered based on business requirements.
Interview Process
The selection process will typically include the following rounds:
Round 1 – Recruiter Screening
- Experience and role fit
- Notice period
- Location and work-mode alignment
- Compensation expectations
- Communication assessment
Round 2 – Architecture Interview
- AI/GenAI architecture
- LLM and RAG architecture
- LangGraph
- Semantic Kernel
- Vector Database architecture
- Enterprise AI Platform
- Cloud AI Architecture
- Responsible AI
- Architecture case studies and system-design scenarios
Round 3 – Leadership / Stakeholder Round
- Technical leadership
- Architecture strategy
- Decision-making
- Stakeholder management
- Team leadership and mentoring
- Enterprise transformation experience
Round 4 – HR Discussion
- Culture fit
- Compensation
- Joining timeline
- Location and work model
- Employment-related discussions
Ideal Candidate Profile
The ideal candidate is a senior AI/GenAI Architect who combines deep architecture expertise with practical experience building enterprise AI solutions.
The candidate should be capable of taking an AI use case from business requirement → architecture → technology selection → PoC → production deployment → governance and optimization.
Strong candidates will demonstrate hands-on knowledge of LangGraph, Semantic Kernel, RAG, Vector Databases, Agentic AI, Enterprise AI Platforms, Responsible AI, and Cloud AI Architecture, along with the ability to define and communicate technology strategy at an enterprise level.