Architect

We're Hiring

Job title: AI Architect

Location: Bangalore

Experience: 8+Years

Notice Period: Immediate Joiners.

AGENTIC SOLUTIONS ARCHITECT

Position Overview

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:

• 8-12 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:
• Agentic AI design patterns (ReAct, Plan-and-Execute, Reflection, Tool-use)
• Multi-agent orchestration architectures
• RAG architecture patterns (naive, advanced, agentic RAG, graph RAG)
• 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 architectures 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:
• Knowledge base design and ontology development
• Index refresh automation and data lifecycle management
• Topic clustering and domain grounding strategies
• Hallucination prevention and mitigation techniques
• 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:
• PII redaction and data privacy controls
• Access governance and role-based permissions (RBAC)
• Secure model deployment and serving
• Prompt injection prevention and input validation
• Audit logging and compliance tracking
• AI Governance: Deep understanding of:
• Responsible AI principles and ethical AI frameworks
• Model risk management frameworks
• Compliance requirements (GDPR, HIPAA, SOC 2)
• Bias detection and fairness metrics
• 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)

Industry Certifications:

• Microsoft Certified: Agentic AI Business Solutions Architect (AB-100)
• AWS Certified Solutions Architect - Professional
• Azure Solutions Architect Expert
• Google Cloud Professional Cloud Architect
• Certified Kubernetes Administrator (CKA) or CKAD
• TOGAF or similar enterprise architecture certification

Domain Expertise:

• Deep experience in finance, banking, healthcare, or manufacturing
• Understanding of domain-specific regulations and compliance requirements
• Experience with ERP systems (SAP, Oracle, Microsoft Dynamics)

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

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

Deliverables

• Enterprise AI reference architecture documents and blueprints
• Agentic AI design patterns and implementation guides
• AI governance frameworks and policy documents
• NFR specifications and architecture decision records (ADRs)
• Evaluation frameworks and quality metrics definitions
• Technology assessment reports and vendor comparisons
• Capability maturity assessments and roadmaps
• Executive presentations and technical strategy documents

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