Senior Technical Lead, AI & Automation

February 19, 2026

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About the position

We are seeking a Senior Technical Lead, AI to serve as the technical authority for AI architecture, system design, and delivery across the AI POD. This role is responsible for shaping how AI solutions are designed, built, deployed, and maintained across LLM agents, RAG systems, to automation workflows and production infrastructure. This is a hands-on technical leadership role, not a people manager. You will guide engineers, influence design decisions, establish standards, and ensure AI solutions are scalable, secure, and production-ready while remaining deeply involved in implementation.

Responsibilities

Act as the technical lead for the AI POD, mentoring engineers and helping unblock complex technical challenges.
Establish engineering standards for AI workflows, agent behavior, evaluation, observability, and lifecycle management.
Ensure consistency across AI solutions while allowing flexibility for rapid experimentation.
Lead the design and implementation of LLM-powered agents, tool-calling frameworks, and multi-step reasoning workflows.
Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.
Ensure workflows are resilient, observable, cost-efficient, and maintainable at scale.
Partner with cloud and infrastructure teams to design scalable AI platforms across GCP, AWS, or Azure.
Ensure AI systems comply with corporate security standards, data privacy requirements, and ethical AI principles.

Requirements

Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related discipline.
7+ years of professional Python development experience (APIs, microservices, automation).
Proven experience with LLMs (OpenAI, Anthropic, Meta, Mistral, etc.) and frameworks like LangChain or LlamaIndex.
Strong knowledge of RAG architectures, vector databases (Pinecone, Weaviate, Chroma, Milvus, Supabase), and semantic search.
Experience with AI-driven automation and orchestration platforms (n8n, Make, Zapier).
Cloud-native experience deploying and operating AI workloads in GCP, AWS, or Azure.
Strong system design skills with the ability to balance speed, quality, and long-term maintainability. Solid understanding of AI prompt engineering best practices.

Nice-to-haves

Experience with containerization/orchestration (Docker, Kubernetes).
MLOps experience with continuous training and deployment workflows.
Frontend development skills (React, Next.js, or similar).
Prior experience in self-storage, real estate, or retail technology environments

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