Senior Solution Architect
- India
Posted yesterday
About the role
Sholinganallur, Tamil Nadu
Job Summary
Role Summary
The Generative AI Architect is responsible for defining, governing, and scaling enterprise‑grade GenAI solutions that are secure, compliant, cost‑effective, and aligned to measurable business outcomes.
This role sits at the intersection of AI engineering, enterprise architecture, cloud platforms, security, and transformation strategy , enabling the organization to move from GenAI experimentation to sustained production value .
Role Objective
To design and institutionalize GenAI platforms and solution patterns that:
Accelerate business productivity and decision‑making
Ensure compliance with regulatory, privacy, and Responsible AI standards
Enable reuse, scale, and consistency across the enterprise
Monetize GenAI capabilities across Run, Change, and Transform initiatives
Key Responsibilities
Key Responsibilities
1. Enterprise GenAI Architecture & Design
Define end‑to‑end GenAI reference architectures , including:
LLM selection (proprietary, open‑source, fine‑tuned)
RAG (Retrieval Augmented Generation)
Agent‑based and orchestration frameworks
Establish build vs buy vs reuse architecture decisions
Translate business use cases into scalable, production‑ready designs
2. Platform & Cloud Enablement
Architect GenAI solutions on enterprise cloud platforms (Azure/AWS/GCP)
Integrate GenAI with:
Core business applications and APIs
Data platforms, warehouses, and knowledge repositories
Legacy and modernization programs
Define standardized GenAI components for reuse across teams
3. Responsible AI, Security & Compliance
Embed Responsible AI principles by design , including:
PII/PHI protection
Prompt safety and guardrails
Transparency, auditability, and traceability
Partner with Legal, Risk, Security, and Compliance teams to ensure:
Regulatory adherence (e.g., GDPR, SOC2, ISO)
Secure access, identity, and data handling
Define governance frameworks for models, prompts, and data usage
4. Scalability, Performance & Cost Governance
Architect for high availability, resilience, and scale
Implement:
Token and inference optimization strategies
Model routing and caching
Usage controls and quota management
Drive AI cost governance (FinOps + MLOps alignment)
5. GenAI Engineering Standards & AI Ops
Define LLMOps / MLOps standards for:
CI/CD pipelines for models and prompts
Model versioning and lifecycle management
Monitoring hallucinations, drift, and performance
Establish operational readiness for production GenAI workloads
6. Business Alignment & Value Realization
Work with business and delivery leaders to:
Prioritize high‑value GenAI use cases
Define success metrics and ROI tracking
Ensure GenAI initiatives deliver measurable outcomes , not just PoCs
Support reuse across portfolios and business units
Key Stakeholders
CIO / CTO / CDO
Enterprise & Solution Architects
Data Science & Engineering teams
Security, Risk, Legal, Compliance
Business and Transformation Leaders
Skill Requirements
Required Skills & Experience
Technical & Architectural
10–15+ years in enterprise architecture / solution architecture
Hands‑on experience with:
Large Language Models (LLMs)
RAG, embeddings, vector databases
API‑first and microservices architectures
Strong cloud architecture experience (Azure preferred in enterprise contexts)
Working knowledge of MLOps / LLMOps practices
Governance & Enterprise Design
Experience designing for regulated environments
Strong understanding of:
Non‑functional requirements (security, scalability, compliance)
Architecture standards and reference models
Business & Leadership
Ability to translate business objectives into architecture decisions
Experience influencing senior stakeholders (Director / VP / CXO)
Strong communication and decision‑making skills
Other Requirements
Success Metrics
Adoption rate of GenAI platforms and services
Reduction in manual effort / cycle time
Cost efficiency per transaction or inference
Reuse of GenAI components across teams
Zero critical security or compliance incidents
Role Positioning
This role is strategic and horizontal , typically part of:
GenAI Center of Excellence (COE)
Enterprise Architecture or Digital Platform teams
Transformation programs focused on modernization and AI‑first delivery
Career Path (Indicative)
Principal Architect – GenAI
Enterprise AI Platform Lead
Head of GenAI / AI COE
Digital / Technology Transformation Leader
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Job Summary
Role Summary
The Generative AI Architect is responsible for defining, governing, and scaling enterprise‑grade GenAI solutions that are secure, compliant, cost‑effective, and aligned to measurable business outcomes.
This role sits at the intersection of AI engineering, enterprise architecture, cloud platforms, security, and transformation strategy , enabling the organization to move from GenAI experimentation to sustained production value .
Role Objective
To design and institutionalize GenAI platforms and solution patterns that:
Accelerate business productivity and decision‑making
Ensure compliance with regulatory, privacy, and Responsible AI standards
Enable reuse, scale, and consistency across the enterprise
Monetize GenAI capabilities across Run, Change, and Transform initiatives
Key Responsibilities
Key Responsibilities
1. Enterprise GenAI Architecture & Design
Define end‑to‑end GenAI reference architectures , including:
LLM selection (proprietary, open‑source, fine‑tuned)
RAG (Retrieval Augmented Generation)
Agent‑based and orchestration frameworks
Establish build vs buy vs reuse architecture decisions
Translate business use cases into scalable, production‑ready designs
2. Platform & Cloud Enablement
Architect GenAI solutions on enterprise cloud platforms (Azure/AWS/GCP)
Integrate GenAI with:
Core business applications and APIs
Data platforms, warehouses, and knowledge repositories
Legacy and modernization programs
Define standardized GenAI components for reuse across teams
3. Responsible AI, Security & Compliance
Embed Responsible AI principles by design , including:
PII/PHI protection
Prompt safety and guardrails
Transparency, auditability, and traceability
Partner with Legal, Risk, Security, and Compliance teams to ensure:
Regulatory adherence (e.g., GDPR, SOC2, ISO)
Secure access, identity, and data handling
Define governance frameworks for models, prompts, and data usage
4. Scalability, Performance & Cost Governance
Architect for high availability, resilience, and scale
Implement:
Token and inference optimization strategies
Model routing and caching
Usage controls and quota management
Drive AI cost governance (FinOps + MLOps alignment)
5. GenAI Engineering Standards & AI Ops
Define LLMOps / MLOps standards for:
CI/CD pipelines for models and prompts
Model versioning and lifecycle management
Monitoring hallucinations, drift, and performance
Establish operational readiness for production GenAI workloads
6. Business Alignment & Value Realization
Work with business and delivery leaders to:
Prioritize high‑value GenAI use cases
Define success metrics and ROI tracking
Ensure GenAI initiatives deliver measurable outcomes , not just PoCs
Support reuse across portfolios and business units
Key Stakeholders
CIO / CTO / CDO
Enterprise & Solution Architects
Data Science & Engineering teams
Security, Risk, Legal, Compliance
Business and Transformation Leaders
Skill Requirements
Required Skills & Experience
Technical & Architectural
10–15+ years in enterprise architecture / solution architecture
Hands‑on experience with:
Large Language Models (LLMs)
RAG, embeddings, vector databases
API‑first and microservices architectures
Strong cloud architecture experience (Azure preferred in enterprise contexts)
Working knowledge of MLOps / LLMOps practices
Governance & Enterprise Design
Experience designing for regulated environments
Strong understanding of:
Non‑functional requirements (security, scalability, compliance)
Architecture standards and reference models
Business & Leadership
Ability to translate business objectives into architecture decisions
Experience influencing senior stakeholders (Director / VP / CXO)
Strong communication and decision‑making skills
Other Requirements
Success Metrics
Adoption rate of GenAI platforms and services
Reduction in manual effort / cycle time
Cost efficiency per transaction or inference
Reuse of GenAI components across teams
Zero critical security or compliance incidents
Role Positioning
This role is strategic and horizontal , typically part of:
GenAI Center of Excellence (COE)
Enterprise Architecture or Digital Platform teams
Transformation programs focused on modernization and AI‑first delivery
Career Path (Indicative)
Principal Architect – GenAI
Enterprise AI Platform Lead
Head of GenAI / AI COE
Digital / Technology Transformation Leader
#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-