Role Overview
We are seeking a highly experienced Senior AI Architect to lead the design and implementation of enterprise-scale Agentic AI systems and multi-agent orchestration platforms. This role requires deep expertise in LLM-based architectures, distributed systems, and cloud-native infrastructure.
As a technical authority, you will guide enterprise clients through their Agentic AI transformation, from evaluating AI frameworks and communication protocols to deploying scalable, production-ready AI automation solutions.
You will work at the forefront of GenAI platform engineering, designing architectures that power intelligent automation, enterprise knowledge systems, and AI-driven workflows.
Key Responsibilities
Agentic AI Architecture & Design
- Design and implement end-to-end multi-agent orchestration systems for enterprise automation and decision intelligence.
- Define agent design patterns, including agent roles, delegation frameworks, task decomposition, and orchestration strategies.
- Architect scalable agent ecosystems with lifecycle management, monitoring, fallback mechanisms, and human-in-the-loop capabilities.
- Evaluate and implement inter-agent communication protocols such as MCP, A2A, REST, gRPC, JSON-RPC, and event-driven messaging.
GenAI & Foundation Model Integration
- Select and integrate LLMs and foundation models (OpenAI, Anthropic, Gemini, Mistral, Llama, etc.) based on task requirements.
- Develop advanced prompt engineering and context management strategies, including:
- Few-shot prompting
- Chain-of-thought reasoning
- Retrieval-Augmented Generation (RAG)
- Structured output pipelines
- Implement tool and function calling patterns enabling agents to interact with enterprise APIs, databases, and services.
- Optimize context window management, token budgets, and dynamic context injection for scalable production systems.
State Management & Agent Memory
- Architect stateful AI systems with short-term, long-term, and episodic memory layers.
- Implement persistence strategies using:
- Vector databases
- Key-value stores
- Graph databases
- Relational systems
- Design auditable and idempotent execution patterns suitable for enterprise governance and compliance requirements.
Microservices & Platform Engineering
- Build AI platforms using loosely coupled microservices with scalable APIs and observability built in.
- Deploy AI systems using container orchestration platforms such as Kubernetes (EKS, AKS, or GKE).
- Establish CI/CD pipelines for AI workloads including model versioning, prompt versioning, and deployment strategies.
- Promote Infrastructure-as-Code (IaC) using tools like Terraform and GitOps deployment practices.
Enterprise Client Engagement
- Partner with enterprise stakeholders to assess AI readiness and automation opportunities.
- Translate complex business requirements into scalable AI system architectures.
- Provide guidance on build vs. buy decisions for AI frameworks and vendor tools.
- Produce architecture documentation, reference designs, and implementation playbooks.
Required Qualifications
- 8+ years of experience in software engineering or platform architecture.
- 3+ years of experience designing AI/ML systems or GenAI platforms.
- Hands-on experience building multi-agent or agentic AI orchestration systems in production.
- Strong experience with agent frameworks such as:
- LangChain
- LangGraph
- AutoGen
- CrewAI
- Semantic Kernel
- Expertise integrating LLMs, embeddings, tool/function calling, and RAG pipelines.
- Deep knowledge of microservices architecture, distributed systems, and API design.
- Experience with container orchestration (Kubernetes preferred) and cloud platforms such as GCP, AWS, or Azure.
- Strong programming skills in Python, with additional experience in TypeScript, Go, or Java preferred.
Preferred Qualifications
- Experience working with enterprise clients or consulting environments.
- Knowledge of AI governance, responsible AI, and compliance frameworks.
- Familiarity with model fine-tuning, RLHF, or adapter-based model customization.
- Experience with AI observability tools such as LangSmith, Arize AI, or OpenTelemetry.
- Experience working with vector databases (Pinecone, Weaviate, Qdrant, pgvector).
- Contributions to open-source AI or agentic system projects.
