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.

LLM & AI Integration :

- 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.

AI Evaluation, Monitoring & Observability :

- 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.

AI Safety, Security & Governance :

- 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.

Technical Leadership & Architecture Assessment :

- 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.

Primary Skills :

- 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.

Secondary 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.