Agentic AI Architect

Aimlroles; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Tiger Analytics is seeking an experienced Agentic AI Architect with Gen AI expertise to design and implement scalable AI agent architectures for enterprise environments. You will lead architectural decisions, build PoCs, and collaborate with product teams to translate business needs into actionable agentic workflows.

Join a fast-growing analytics consultancy serving Fortune 500 clients, steering governance, ethics, and secure, cost-efficient AI deployment across Azure, AWS, and GCP ecosystems.

Qualifications

• 5+ years of cloud architecture on at least one hyperscaler (Azure/AWS/GCP).
• 2+ years with LLMs & AI applications (RAG, autonomous agents).
• Hands-on with orchestration frameworks like LangGraph, LangChain, or similar.

Responsibilities

• Define robust, secure, and scalable architectures for autonomous AI agents and cognitive workflows.
• Actively build PoCs/MVPs to validate architectures and de-risk complex agent workflows.
• Translate business problems into structured agentic workflows and communicate ROI to leadership.

Skills

Cloud architecture

AI/LLM engineering

Agentic frameworks

Programming (Python/TypeScript/Java)

CI/CD for LLM Ops

Vector databases

Education

Bachelor’s or Master’s in CS/DS

Tools

LangGraph

Semantic Kernel

AutoGen

CrewAI

LangChain

Job description

Tiger Analytics is looking for experienced Agentic AI Architect with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

Requirements

• 1. Architecture & Solution Design (50%)
  • Define the Blueprint: Design robust, secure, and scalable architectures for autonomous and semi-autonomous AI agents, multi-agent orchestration systems, and cognitive workflows.
  • Hyperscaler Implementation: Architect cloud-native AI pipelines leveraging specialized services across Azure (e.g., Azure OpenAI, AI Studio, Azure Functions), AWS (e.g., Bedrock, SageMaker, AWS Lambda), or GCP (e.g., Vertex AI, Agent Builder, Cloud Run).
  • System Orchestration & Memory: Establish architectural patterns for LLM orchestration (LangChain, LangGraph, AutoGen, CrewAI), semantic memory storage (vector databases like Pinecone, Milvus, or cloud-native vector extensions), and state management.
  • Integration & Scalability: Ensure seamless integration of AI agents with legacy enterprise systems, APIs, RPA tools, and real-time data streams while optimizing for cost, latency, and throughput.
• 2. Hands-on Engineering & Prototyping (30%)
  • Rapid Prototyping: Actively write code to build Proof of Concepts (PoCs) and minimum viable products (MVPs) to validate architectural theories and de-risk complex agent workflows.
  • Code Quality & Governance: Establish production-grade coding standards for AI engineering. Conduct deep-dive code reviews to ensure reliability, security, and optimal prompt/agent behavior.
  • Framework Evaluation: Hands‑on evaluation of emerging AI frameworks, tooling, and foundational models (open‑source vs. proprietary) to determine enterprise viability.
• 3. Stakeholder Collaboration & Leadership (20%)
  • Translate Business to Tech: Partner with product managers and business stakeholders to translate vague business problems into structured agentic workflows and concrete technical requirements.
  • Technical Advocacy: Act as the subject matter expert on Agentic AI, educating internal engineering teams and advising executives on capabilities, limitations, and ROI.
  • AI Governance & Safety: Collaborate with Security, Compliance, and Risk teams to architect robust AI guardrails (e.g., NeMo Guardrails, Llama Guard), ensuring data privacy, hallucination mitigation, and ethical AI deployment.

Required Qualifications

• Technical Experience:
  • Cloud Expertise: 5+ years of experience architecting cloud solutions on at least one major hyperscaler (Azure, AWS, or GCP), with a deep understanding of cloud‑native microservices and serverless paradigms.
  • AI/LLM Engineering: 2+ years of direct experience working with Large Language Models (LLMs) and building complex AI applications (RAG pipelines, autonomous agents, tool‑calling systems).
  • Agentic Frameworks: Hands‑on proficiency with orchestration frameworks such as LangGraph, Semantic Kernel, AutoGen, CrewAI, or LangChain.
  • Development Skills: Strong production‑grade programming skills in Python (preferred), TypeScript, or Java, along with experience in CI/CD pipelines for AI (LLMOps).
  • Data & Search Infrastructure: Proven experience with vector databases and enterprise search engines (e.g., Azure AI Search, Amazon OpenSearch, Vertex AI Search).
• Leadership & Soft Skills:
  • Bilingual Communicator: Ability to explain complex machine learning or architectural concepts simply to non‑technical business leaders.
  • Pragmatic Mindset: Balanced approach between pioneering bleeding‑edge AI trends and delivering stable, cost‑effective enterprise software.
  • Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related technical field (or equivalent practical experience).
• Preferred Certifications
  • Azure Solutions Architect Expert / Azure AI Engineer Associate
  • AWS Certified Solutions Architect – Professional / AWS Certified Machine Learning – Specialty
  • Google Cloud Professional Cloud Architect / Professional Machine Learning Engineer

Applicants based in US and Canada are invited to apply.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast‑growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.