Offer summary

Qualifications:

3+ years of experience in machine learning engineering or applied NLP., Proven track record of building LLM-powered applications and strong experience with foundation models., Strong programming skills in Python and familiarity with AI frameworks like Hugging Face and LangChain., Deep understanding of retrieval-augmented generation and AI performance evaluation..

Key responsibilities:

Develop and integrate LLM-powered features for workflow automation and content strategy.

Optimize AI performance through prompt engineering and evaluation frameworks.

Build and scale AI infrastructure for low-latency responses and cost-efficient model usage.

Collaborate with product and engineering teams to enhance AI-driven user experiences.

Job description

About AirOps

Today thousands of leading brands and agencies use AirOps to win the battle for attention with content that both humans and agents love.

We’re building the platform and profession that will empower a million marketers to become modern leaders — not spectators — as AI reshapes how brands reach their audiences.

We’re backed by awesome investors, including Unusual Ventures, Wing VC, Founder Collective, XFund, Village Global, and Alt Capital, and we’re building a world-class team with in-person hubs in San Francisco, New York, and Montevideo, Uruguay.

About the Role

We're looking for a product-minded AI engineer to help us rapidly define and ship features that make all AirOps customers 10x content engineers.

In this role, you'll drive the development of advanced AI systems, including our SEO Strategy Agent and Workflow Builder Copilot. You'll lead the creation of intelligent solutions that empower businesses to optimize their SEO strategy and streamline workflow creation with AI-powered assistance.

Joining at a critical juncture, you'll collaborate closely with product management to shape our AI roadmap and technical architecture.

Responsibilities

Develop and integrate LLM-powered features such as AI-assisted workflow automation, code generation, and content strategy.
Optimize AI performance through prompt engineering, retrieval-augmented generation (RAG), and evaluation frameworks.
Build and scale AI infrastructure, ensuring low-latency responses, caching, and cost-efficient model usage.
Implement AI observability and safeguards, monitoring quality, security, and compliance.
Collaborate with product and engineering teams to deliver intuitive, AI-driven user experiences.
Stay ahead of AI advancements, continuously improving our AI-powered capabilities.
Collaborate with early adopters to optimize model performance and usability.

Qualifications

3+ years of experience in machine learning engineering, AI/LLM integration, or applied NLP
Proven track record of building LLM-powered applications
Strong experience with foundation models (GPT-4o, Claude, etc.) and advanced prompt engineering
Experience with embedding models (e.g., OpenAI Ada, Cohere, or local vector stores like pgvector, Weaviate, Pinecone)
Deep understanding of retrieval-augmented generation (RAG) and contextual AI response optimization
Familiarity with LangChain, or similar frameworks for orchestrating LLM-powered applications
Strong programming skills in Python (experience with AI frameworks like Hugging Face, LangChain, or OpenAI SDK)
Experience in evaluating LLM performance, running A/B tests, and implementing feedback loops for AI refinement
Solid understanding of caching, rate limiting, and cost optimization strategies for AI workloads
Ability to work cross-functionally with engineers, product managers, and end users to develop impactful AI solutions