We are seeking a Principal-level Agentic Solutions Architect to define and drive the enterprise AI architecture strategy, with a specific focus on agentic AI systems, multi-agent orchestration, and production-scale Generative AI implementations. This role requires deep technical expertise, strategic thinking, and the ability to translate business requirements into scalable, secure, and governable AI architectures.

Must-Have Skills & Experience :

Experience Requirements :

- 12-15 years of total technical experience with minimum 3-5 years focused on AI/ML architecture

- Proven track record architecting and delivering 10+ enterprise-scale AI/ML solutions

- Experience leading cross-functional teams and driving technical strategy

- Track record of designing systems that handle millions of transactions or high-volume workloads

- Experience presenting to C-level executives and translating technical concepts to business stakeholders

Core Architectural Skills :

- Solution Architecture : Expert-level systems design with focus on scalability, reliability, and maintainability

- AI Architecture Patterns : Deep knowledge of :

1. Agentic AI design patterns (ReAct, Plan-and-Execute, Reflection, Tool-use)

2. Multi-agent orchestration architectures

3. RAG architecture patterns (naive, advanced, agentic RAG, graph RAG)

4. Workflow orchestration patterns (prompt chaining, routing, parallelization)

- Enterprise Integration : Expertise in integrating AI systems with enterprise applications (ERP, CRM, data warehouses)

- Cloud Architecture : Advanced knowledge of cloud-native architectures on Azure, AWS, or GCP

- Microservices & APIs : Deep understanding of microservices architecture, API design, and distributed systems

- OpenAI Agents SDK / Responses API

Agentic AI Expertise :

- Expert knowledge of agent architecture including planning engines, reasoning frameworks, and tool orchestration

- Experience designing multi-agent systems with agent-to-agent communication protocols

- Understanding of agentic workflow tiers (Foundation, Workflow, Autonomous)

- Knowledge of agent memory architectures (task memory, vector memory, episodic memory)

- Experience with Model Context Protocol (MCP) and Agent2Agent (A2A) standards

- Human-in-the-loop escalation architecture design

RAG & Knowledge Systems :

- Expert-level RAG architecture design including :

1. Knowledge base design and ontology development

2. Index refresh automation and data lifecycle management

3. Topic clustering and domain grounding strategies

4. Hallucination prevention and mitigation techniques

5. Hybrid search and knowledge graph integration

- Experience with graph databases (Neo4j, TigerGraph) for knowledge representation

- Understanding of semantic layer architecture for enterprise data

MLOps & Platform :

- Deep understanding of MLOps architecture and deployment patterns

- Experience with Kubernetes for ML workload orchestration

- Knowledge of model governance, versioning, and lifecycle management

- Experience designing observability and monitoring frameworks for AI systems

- Understanding of CI/CD pipelines for ML applications

Security & Governance :

- Enterprise Security : Expertise in :

1. PII redaction and data privacy controls

2. Access governance and role-based permissions (RBAC)

3. Secure model deployment and serving

4. Prompt injection prevention and input validation

5. Audit logging and compliance tracking

- AI Governance : Deep understanding of :

1. Responsible AI principles and ethical AI frameworks

2. Model risk management frameworks

3. Compliance requirements (GDPR, HIPAA, SOC 2)

4. Bias detection and fairness metrics

5. Explainability and interpretability requirements

Technical Foundation :

- Strong programming background (Python, Java, or similar)

- Deep understanding of LLM capabilities and limitations

- Knowledge of multiple LLM providers (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, GCP Vertex)

- Understanding of cost optimization strategies for LLM deployments

- Experience with prompt engineering and optimization at scale

Good-to-Have Skills :

Advanced Capabilities :

- Experience with specific frameworks : LangGraph, CrewAI, AutoGen, Semantic Kernel, Haystack

- Knowledge of distributed agent policy enforcement

- Experience with agent self-reflection and adaptation frameworks

- Understanding of agent registry and capability matching systems

- Knowledge of constrained autonomy zones and validation checkpoints

Enterprise Architecture :

- Experience with enterprise architecture frameworks (TOGAF, Zachman)

- Knowledge of data mesh and data fabric architectures

- Experience with event-driven architectures and streaming platforms (Kafka, Pulsar)

- Understanding of feature stores and model serving platforms

Advanced AI Topics :

- Experience with fine-tuning and domain adaptation strategies

- Knowledge of model compression and optimization techniques

- Understanding of federated learning and privacy-preserving ML

- Experience with multimodal AI systems (text, image, audio)

Key Responsibilities :

Architecture & Design :

- Define enterprise AI reference architectures and design patterns

- Design agentic AI solutions that meet business objectives while ensuring scalability and security

- Create architecture blueprints including system diagrams, data flow diagrams, and sequence diagrams

- Define NFRs (non-functional requirements) including performance, security, and scalability targets

- Conduct architecture reviews and provide guidance on technical decisions

Strategy & Governance :

- Develop AI governance frameworks and establish best practices

- Define evaluation frameworks and quality metrics for AI applications

- Create risk assessment and mitigation strategies for AI deployments

- Establish security and compliance controls for AI systems

- Define cost optimization strategies and resource allocation models

AI & Application Engineering :

- Architect intelligent features such as recommendation engines, conversational interfaces, predictive analytics, copilots, and workflow automation within applications.

- Integrate external AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI, etc.) and build internal micro-models where needed.

- Evaluate feasibility, performance, and stability of AI models within production applications.

- Develop reusable components, accelerators, toolkits, and reference architectures for AI-infused app development.

Leadership & Collaboration :

- Lead architecture discussions with product, engineering, and business stakeholders

- Mentor senior engineers and provide technical guidance to development teams

- Collaborate with enterprise architects and platform teams on cross-functional initiatives

- Present technical strategies and recommendations to executive leadership

- Drive adoption of best practices across the organization

Innovation & Maturity :

- Define AI maturity roadmaps and capability-building plans

- Evaluate new technologies and assess their fit for enterprise needs

- Conduct proof-of-concept initiatives for emerging AI capabilities

- Define Center of Excellence (CoE) structure and operating models

- Create internal IP and reusable accelerators

Delivery & Execution :

- Lead technical delivery for multiple cross-functional squads, ensuring high-quality releases, optimal performance, and system reliability.

- Oversee code reviews, design reviews, and technical grooming sessions.

- Drive continuous integration/continuous deployment (CI/CD), DevSecOps practices, and observability across AI-driven workloads.

- Identify risks, dependencies, and technical challenges proactively and drive mitigation strategies.

- Contribute to creating :

1. Enterprise AI reference architecture documents and blueprints

2. Agentic AI design patterns and implementation guides

3. AI governance frameworks and policy documents

4. NFR specifications and architecture decision records (ADRs)

5. Evaluation frameworks and quality metrics definitions

6. Technology assessment reports and vendor comparisons

Educational Requirements :

- Bachelor's degree in Computer Science, Engineering, Information Technology, or related field (required)

- Master's degree in Computer Science, AI/ML, Systems Architecture, or MBA preferred

- Relevant architecture or AI certifications highly valued

Soft Skills :

- Strategic Thinking : Ability to align technical solutions with business strategy

- Leadership : Strong technical leadership with ability to influence without authority

- Communication : Exceptional communication skills - can articulate complex technical concepts to diverse audiences (executives, engineers, business stakeholders)

- Problem-Solving : Structured approach to solving ambiguous, complex problems

- Collaboration : Excellent stakeholder management and cross-functional collaboration skills

- Pragmatism : Ability to balance ideal architecture with practical constraints (budget, timeline, skill availability)

- Continuous Learning : Commitment to staying current with rapidly evolving AI technologies