Lead AI Architect & Delivery Manager

CanadaRemotelead

OperationsProject Manager

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Quick Summary

Key Responsibilities

Own the end-to-end architecture of the Snowflake Cortex RAG pipeline,

Requirements Summary

Strong Production Python development, with deep experience in Asyncio, FastAPI, design patterns, and distributed systems. Agentic Framework Engineering: 2+ years of direct,

Technical Tools

OperationsProject Manager

The Lead AI Architect & Delivery Manager will serve as the technical authority and engagement lead for the client's TPRM AI Assistant project, backfilling the departing AI Lead. This role is responsible for driving the technical strategy of the Snowflake-native RAG solution, orchestrating the offshore engineering team, interfacing directly with FCB leadership (ED&A, TPRO, InfoSec SMEs), and ensuring the solution meets its tight production launch milestone.

Responsibilities

~1 min read

→Technical & Strategic Leadership: Own the end-to-end architecture of the Snowflake Cortex RAG pipeline, ensuring alignment with Enterprise TPRM Standards
→Client & Stakeholder Management: Act as the primary technical liaison to the client's business owners, AI leads, and InfoSec SMEs.
→UAT Execution & Triage: Lead User Acceptance Testing (UAT) sessions with InfoSec SMEs, converting user feedback into prioritized technical backlog items for the offshore pod.
→AI Governance & Compliance: Own the preparation of the Model Development Documentation (MDD) packet and drive AIEC-2 (Effective Challenge Tier 2) clearance ahead of production deployment.
→Delivery Oversight: Direct daily agile standups and sprint planning for the 3-person offshore pod to ensure milestone delivery within the SOW budget.

Requirements

~1 min read

Hands-on Python Expertise: Strong Production Python development, with deep experience in Asyncio, FastAPI, design patterns, and distributed systems.
Agentic Framework Engineering: 2+ years of direct, hands-on experience building, deploying, and debugging autonomous multi-agent systems in production using LangGraph, AutoGen, CrewAI, or LlamaIndex.
Function Calling & Structured Outputs: Proven ability to build structured generation pipelines (using Pydantic, Instructor, or native function schemas) to bridge LLMs with traditional software APIs.
Vector & Relational Storage: Hands-on experience with databases such as PostgreSQL/pgvector, Pinecone, Qdrant, Milvus, or enterprise AI DBs (Oracle 23ai).
Testing & CI/CD for AI: Experience writing unit tests, integration tests, and regression evaluations for probabilistic, LLM-driven applications.

What We Offer

~1 min read

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.