AI Engineer - US

ScaleOps; United States; Presencial; USD 140.000 - 210.000; Jornada completa

Descripción de la vacante

ScaleOps in the United States is building an AI group and seeks a Senior AI Engineer to shape it from the ground up. You'll design autonomous AI agents, integrate with Slack/Jira, and push production-ready AI that drives the core automation engine with real business impact.

Ideal candidates have 4+ years Python, GenAI in production, and a builder mindset. You will own AI systems end-to-end, participate in technical leadership, and help ScaleOps reduce costs while accelerating innovation.

Formación

• 4+ years of software engineering experience with Python.
• Experience delivering production GenAI systems (latency, cost control).
• Experience building/operating production cloud-based systems.
• Ability to take ownership and work across teams to drive results.

Responsabilidades

• Design autonomous AI agents that analyze infrastructure in real time and make decisions.
• Develop integrations with Slack, Jira and AI IDEs for context-aware insights.
• Build and deploy ML models, from training to production, with monitoring.
• Lead AI architecture decisions as a founding member of the AI group.
• Ensure AI systems are fast, reliable, secure and cost-effective.

Conocimientos

Python proficiency

Backend engineering

LLM production experience

Ownership independence

Educación

Bachelor's degree in Computer Science or related

Herramientas

LangGraph

PydanticAI

Slack

Jira

Cursor

Windsurf

LangChain

MetaGPT

Descripción del empleo

ScaleOps is redefining autonomous cloud and AI infrastructure. We're on a mission to free DevOps and platform engineers from manual resource management so they can focus on innovation, not tuning resources. The results: maximized performance and a reduction of cloud costs by up to 80%.

As the category leader in Autonomous Cloud and AI Infrastructure Resource Management, we're trusted by leading enterprises including Adobe, Wiz, Epic Games, Northwestern Mutual, Coinbase, DocuSign, and Fortune 100 companies to autonomously manage their most critical production environments.

Backed by Insight Partners, Lightspeed Venture Partners, and other leading VCs with over $210M in funding, ScaleOps is the leading player in a massive and growing market. We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute.

What You'll Be Doing

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the ScaleOps platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

• Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
• Platform Integration & Intelligent Decision Systems: Develop MCPs to expose ScaleOps capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
• AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
• R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
• AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
• End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
• Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how ScaleOps evolves from AI-enhanced internal tools to customer-facing AI products.

What You'll Bring

• Core Engineering: Significant software engineering experience (typically 4+ years) with strong Python skills and solid backend engineering fundamentals.
• Production Experience: Experience building and operating production systems in cloud environments.
• Real-World GenAI Experience: Practical experience bringing LLM-based systems into production, including handling latency, cost control, and failure modes. Familiarity with additional agentic frameworks (e.g., LangChain, MetaGPT) and evaluation frameworks.
• Builder Mentality: Strong ownership and the ability to operate independently while collaborating closely across teams, with the motivation to grow into technical leadership as the group expands.
• (Advantage) Data & RAG: Experience enabling LLMs to consume structured or operational data (configurations, logs, metrics) and experience with retrieval systems (RAG) or vector databases.