Sr. AI Ops Software Engineer

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Requirements:

• 5+ years of software engineering experience, with at least 2+ years working with AI-powered applications or a strong interest in learning AI technologies
• Strong experience with JavaScript/TypeScript, Python, and AWS serverless (Lambda, Step Functions, SQS, Aurora)
• Deep understanding of AI/ML concepts, including prompt engineering, OpenAI APIs, and Retrieval-Augmented Generation (RAG) with vector databases (Pinecone, Weaviate, etc.), or a strong willingness to learn
• Proficiency in building and scaling serverless applications and handling DevOps responsibilities (CI/CD, monitoring, infrastructure as code)
• Experience optimizing AI API costs and performance for real-world applications
• Strong problem-solving skills with the ability to design and implement complex AI-powered solutions
• Familiarity with data pipelines and analytics platforms (Metabase, Snowflake, or similar BI tools) is a plus
• Strong communication and collaboration skills, with experience working across teams (Ops, Sales, CS, etc.)
• Self-starter mindset, comfortable taking ownership of applications and working in a fast-moving, remote environment with autonomy

Responsibilities:

• Own and lead the development of AI-powered applications that streamline operations, primarily using AWS serverless technologies (Lambda, Aurora, S3, SQS) and integrating with tools like Slack and HubSpot
• Design and implement new features based on operational needs, working closely with internal stakeholders
• Productize new and existing AI solutions, with a 20% focus on researching new AI technologies and 80% focus on deploying AI-powered tools into production
• Optimize AI workflows and prompt engineering, ensuring the effectiveness of AI models (primarily built on OpenAI APIs)
• Architect and scale infrastructure, including serverless computing, databases, and vector search technologies (e.g., Pinecone) for Retrieval-Augmented Generation (RAG) applications
• Enhance DevOps practices, leading the adoption of CI/CD pipelines with GitHub Actions to automate deployments and improve engineering velocity
• Ensure cost-efficient AI and cloud operations, optimizing usage of OpenAI, AWS, Pinecone, and other third-party services
• Implement monitoring, logging, and usage analytics to improve application reliability and adoption
• Collaborate with data engineers on integrating AI applications with broader data infrastructure and business intelligence tools
• Evaluate build-vs-buy decisions, assessing third-party AI solutions and recommending whether to develop in-house tools or integrate external services
• Stay ahead of AI trends, researching emerging technologies in AI/ML, vector search, and automation to inform future initiatives
• A strong desire to understand how our GTM teams operate, including their sales processes and workflows within our CRM, HubSpot, to better tailor AI solutions that drive efficiency and impact.