Agentic AI Architect
New
T
Tiger AnalyticsAI consulting
Applicants based in US and Canada are invited to apply.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience architecting cloud solutions on at least one major hyperscaler; 2+ years of direct experience working with Large Language Models (LLMs) and building complex AI applications.
- Required Skills
- AWSPythonGCPAzureCI/CDLLM
Requirements
- Have 5+ years of experience architecting cloud solutions on at least one major hyperscaler: Azure, AWS, or GCP.
- Understand cloud-native microservices and serverless paradigms.
- Have 2+ years of direct experience with LLMs and building complex AI applications, including RAG pipelines, autonomous agents, or tool-calling systems.
- Demonstrate hands-on proficiency with LangGraph, Semantic Kernel, AutoGen, CrewAI, or LangChain.
- Bring production-grade programming skills in Python, TypeScript, or Java.
- Have experience with CI/CD pipelines for AI (LLMOps).
- Have proven experience with vector databases and enterprise search engines.
- Hold a bachelor’s or master’s degree in Computer Science, Data Science, or a related technical field, or equivalent practical experience.
- Explain complex machine learning or architectural concepts simply to non-technical business leaders.
Responsibilities
- Design secure, scalable architectures for autonomous and semi-autonomous agents, multi-agent orchestration systems, and cognitive workflows.
- Architect cloud-native AI pipelines using services across Azure, AWS, or GCP.
- Establish patterns for LLM orchestration, semantic memory storage, and state management.
- Integrate AI agents with enterprise systems, APIs, RPA tools, and real-time data streams, optimizing cost, latency, and throughput.
- Build proof-of-concept and minimum viable product prototypes to validate architectures and agent workflows.
- Establish production-grade AI engineering standards and conduct code reviews.
- Evaluate AI frameworks, tooling, and foundational models for enterprise viability.
- Translate business problems into agentic workflows and technical requirements with product managers and business stakeholders.
- Advise executives and engineering teams on Agentic AI capabilities, limitations, and ROI.
- Collaborate with Security, Compliance, and Risk teams on AI guardrails, privacy, hallucination mitigation, and ethical deployment.
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