AI Ops

A.Team

AI Ops Engineer responsible for turning concepts into AI-driven prototypes and production systems. Collaborating with clients and stakeholders for effective deployment in AI solutions.

Posted 5/5/2026full-timeNew York City • New York • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

JavaScriptNext.jsNode.jsPythonTypeScript

About the role

Key responsibilities & impact

Prototype for Impact: Rapidly build working demos and prototypes in TypeScript and Python to validate ideas and proposals, winning hearts and minds in record time.
Build Enterprise-Grade Platforms: Take prototypes from demo to production — designing scalable, secure, observable systems that hold up to Fortune 500 client requirements.
Own Solutions End-to-End: Break down customer problems into clear goals, design data flows and AI systems (RAG, LLM orchestration, agentic workflows, evals, fine-tuning), and deliver functional, deployed solutions.
Engage Clients Directly: Lead working sessions, demos, and technical discussions with stakeholders ranging from engineering counterparts to executive sponsors. Translate technical decisions into business outcomes and vice versa.
Leverage AI Productivity Tools: Use modern AI coding and productivity tools (Claude Code, Cursor, Copilot, agentic dev environments) as a force multiplier — and help define how the team gets the most out of them.
Collaborate Across Functions: Work closely with product managers (acting as solution partners), designers, AI researchers, and the platform team to turn abstract problems into tangible, tested products.
Drive Velocity Without Sacrificing Quality: Move fast, but ship code that's tested, observable, and ready for the next stage of the engagement.
Push AI Forward: Apply modern AI engineering practices — data pipelines, retrieval systems, LLM evaluation, prompt and context engineering, fine-tuning — to create novel, AI-powered product experiences.

Requirements

What you’ll need

Exceptional Client-Facing Communication: You can run a client meeting, defuse tension, set expectations, and explain a tradeoff to a non-technical executive without losing the technical audience in the room. (This is the single most important attribute for this role.)
Enterprise-Grade Platform Experience: Demonstrated experience designing and shipping production systems for real users — auth, multi-tenancy, observability, security, reliability — not just demos.
Strong TypeScript and Python Proficiency: Comfortable owning full-stack work across both languages; able to move between a Next.js/Node frontend and a Python AI/data backend without friction.
Deep Hands-On AI Systems Experience: Built and shipped systems using LLMs, RAG, embeddings, evals, and modern orchestration frameworks. You understand the difference between a prompt that works once and a system that works at scale.
Proficiency with AI Productivity Tools: You don't just use coding agents and AI tools — you've integrated them into your own workflow in a way that meaningfully changes your output.
0→1 Product Mindset: Experience building products from scratch — comfortable with ambiguity, scoping MVPs, and iterating quickly based on real client feedback.
End-to-End Ownership: Not just a coder. You define the problem, architect the solution, and execute it.
Problem-Solving and Innovation: Track record of finding creative paths through hard, ambiguous problems.
Collaboration: A genuine team player who raises the level of everyone around them.

Benefits

Comp & perks

Equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status.
Proud to be an equal opportunity workplace.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

TypeScriptPythonAI systemsLLM orchestrationdata pipelinesRAGembeddingsprompt engineeringcontext engineeringfull-stack development

Soft Skills

client-facing communicationproblem-solvinginnovationcollaborationend-to-end ownershipadaptabilityteam playertechnical discussionstakeholder engagementsetting expectations