AI Solution Architect

About the Role

We are seeking a leader to build enterprise Generative and Agentic AI platforms featuring high-performance RAG pipelines and vector database integrations, while defining multi-agent collaboration patterns, memory management, and autonomous planning frameworks.

Key Responsibilities

• Architecture Design: Build enterprise Generative and Agentic AI platforms featuring high-performance RAG (Retrieval-Augmented Generation) pipelines and vector database integrations.
• Agent Orchestration: Define multi-agent collaboration patterns, memory management, and autonomous planning frameworks.
• Governance & Security: Implement data privacy, compliance, risk mitigation, and evaluation guardrails across all AI touchpoints.
• Cross-functional Leadership: Guide and mentor engineering teams, run discovery workshops with stakeholders, and define reusable deployment patterns.

Role Summary & Objectives

• Translate business automation and efficiency goals into scalable, production-grade AI architectures.
• Lead the design of autonomous multi-agent systems, complex reasoning loops, and tool-use workflows.
• Establish robust guardrails, human-in-the-loop decision controls, and system observability.

Technical Stack

• Retrieval-Augmented Generation (RAG) pipelines, semantic caching, and context window optimization.
• Function calling, tool use, and structured data extraction schemas.
• Evaluation metrics, tracing, and hallucination reduction guardrails.
• Designing agentic-first workflows and autonomous decision loops.
• Multi-agent coordination patterns (supervisor-worker, decentralized collaboration, stateful graphs).
• Frameworks like LangChain/LangGraph/Bedrock Core Runtime for state and memory management.
• Emerging interoperability standards like Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols.
• Vector databases (e.g., Milvus, Amazon Aurora PostgreSQL) for high-speed similarity search.
• Data pipelines and embedding generation workflows using Python, FastAPI, or Apache Spark.
• Cloud-native deployment on platforms like AWS (Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, DocumentDB).
• Containerization and orchestration tools including Docker and Kubernetes.
• CI/CD pipelines for automated testing of non-deterministic AI outputs.
• Observability and logging pipelines for tracking agent token usage, latency, and failure states.
• Responsible AI frameworks, data privacy compliance, and bias mitigation guardrails.