Reflections Info Systems - Lead AI/Agentic Platform Architect

Include

Agentic AI Architecture & Platform Engineering :

• Design and architect scalable, enterprise-grade agentic AI platforms and solutions.
• Define agent orchestration patterns, runtime architecture, workflows, and multi-agent communication mechanisms.
• Architect prompt engineering frameworks, tool calling, connectors, and model integration strategies.
• Design memory management, state persistence, and context management mechanisms.
• Evaluate and implement RAG architectures and vector database integrations wherever applicable.

• Design and integrate LLM-based applications using leading commercial and open-source models.
• Establish model integration patterns, routing, fallback mechanisms, and model selection strategies.
• Define reusable components and frameworks for agent development and deployment.
• Optimize model performance, latency, scalability, and token consumption.

• Define evaluation frameworks to measure agent accuracy, reliability, consistency, and performance.
• Establish mechanisms for testing prompts, tools, workflows, and end-to-end agent behaviour.
• Implement telemetry, monitoring, tracing, and observability for agentic systems.
• Define mechanisms to identify hallucinations, failures, and performance bottlenecks.

• Identify and address security and governance gaps in agentic AI architectures.
• Establish guardrails for responsible AI implementation, including access controls and data protection.
• Define policies for tool permissions, human-in-the-loop controls, and agent execution boundaries.
• Ensure appropriate compliance, auditability, and governance standards are incorporated into the architecture.

• Assess existing AI and agentic platform architectures and map technical dependencies.
• Identify architecture gaps, integration challenges, scalability risks, and opportunities for improvement.
• Recommend target-state architecture and implementation roadmaps.
• Collaborate with engineering, product, and business stakeholders to translate requirements into technical solutions.
• Provide technical guidance and architectural direction to engineering teams.

• 5+ years of experience in software architecture, AI engineering, platform engineering, or related technical roles, including recent hands-on experience building LLM-powered and agentic AI systems.
• Strong understanding of agent orchestration frameworks, agent runtimes, multi-agent systems, and workflow automation.
• Hands-on experience with LLM integrations, prompt engineering, function calling, and tool orchestration.
• Experience designing memory, state management, context handling, and RAG-based architectures.
• Knowledge of vector databases, embeddings, semantic search, and retrieval mechanisms.
• Experience implementing AI guardrails, evaluation frameworks, and observability solutions.
• Strong understanding of AI security, governance, data privacy, and responsible AI principles.
• Ability to assess complex architectures, identify dependencies, and recommend scalable technical solutions.
• Strong communication and stakeholder management skills.

• Experience with frameworks and platforms such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent.
• Exposure to cloud-based AI ecosystems such as Azure OpenAI, AWS Bedrock, or Google Vertex AI.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, or Azure AI Search.
• Familiarity with agent evaluation tools, tracing, and monitoring frameworks.
• Experience designing enterprise AI platforms with security, scalability, and cost optimization considerations.

(ref:hirist.tech)