Senior AI Engineer

Summary

Join Apollo.io's AI Engineering team as a Senior AI Engineer to build and productionize advanced AI systems using Large Language Models (LLMs) and intelligent agents. You will work on critical Apollo capabilities, including the AI Assistant, Autonomous AI Agents, and more, directly impacting millions of users' productivity. The AI team's mission is to leverage Apollo's data and AI to understand and predict user behaviors, personalize experiences, and optimize the customer journey through intelligent automation. This role requires extensive experience in LLM applications, agent development, and prompt engineering. You will design and deploy production LLM systems, develop sophisticated AI agents, and optimize prompting strategies. Collaboration with product teams and backend engineers is essential. Apollo offers a remote-first, inclusive work environment focused on operational excellence and employee growth.

Requirements

• 8+ years of software engineering experience with a focus on production systems
• 1.5+ years of hands-on LLM experience (2023-present) building real applications with GPT, Claude, Llama, or other modern LLMs
• Demonstrated experience building customer-facing, scalable LLM-powered products with real user usage (not just POCs or internal tools)
• Experience building multi-step AI agents, LLM chaining, and complex workflow automation
• Deep understanding of prompting strategies, few-shot learning, chain-of-thought reasoning, and prompt optimization techniques
• Expert-level Python skills for production AI systems
• Strong experience building scalable backend systems, APIs, and distributed architectures
• Experience with LangChain, LlamaIndex, or other LLM application frameworks
• Proven ability to integrate multiple APIs and services to create advanced AI capabilities
• Experience deploying and managing AI models in cloud environments (AWS, GCP, Azure)
• Experience implementing rigorous evaluation frameworks for LLM systems including accuracy, safety, and performance metrics
• Understanding of experimental design for AI system optimization
• Experience with production monitoring, alerting, and debugging complex AI systems
• Experience building and maintaining scalable data pipelines that power AI systems

Responsibilities

• Build sophisticated multi-agent systems that can reason, plan, and execute complex sales workflows
• Develop systems that maintain conversational context across complex multi-turn interactions
• Build scalable large language model and agentic platforms that enable widespread adoption and viability of agent development within the Apollo ecosystem
• Build back-end systems necessary to support the agents
• Build AI features such as Conversational AI, Natural Language Search, Personalized Email Generation and similar AI features
• Develop and improve recommendation systems and search relevance algorithms
• Build models for automatic company keywords, people keywords, and industry classification
• Create intelligent matching and suggestion engines
• Design and Deploy Production LLM Systems: Build scalable, reliable AI systems that serve millions of users with high availability and performance requirements
• Agent Development: Create sophisticated AI agents that can chain multiple LLM calls, integrate with external APIs, and maintain state across complex workflows
• Prompt Engineering Excellence: Develop and optimize prompting strategies, understand trade-offs between prompt engineering vs fine-tuning, and implement advanced prompting techniques
• System Integration: Build robust APIs and integrate AI capabilities with existing Apollo infrastructure and external services
• Evaluation & Quality Assurance: Implement comprehensive evaluation frameworks, A/B testing, and monitoring systems to ensure AI systems meet accuracy, safety, and reliability standards
• Performance Optimization: Optimize for cost, latency, and scalability across different LLM providers and deployment scenarios
• Cross-functional Collaboration: Work closely with product teams, backend engineers, and stakeholders to translate business requirements into technical AI solutions

Preferred Qualifications

• You've built AI systems that real users depend on, not just demos or research projects
• You understand the difference between a working prototype and a production-ready system
• You have experience with user feedback, iterative improvements, and feedback systems
• You can design end-to-end systems, including back-end systems, asynchronous workflows, LLMs, and agentic systems
• You understand the cost-benefit trade-offs of different AI approaches
• You've made decisions about when to use different LLM providers, fine-tuning vs prompting, and architecture choices
• You implement repeatable, quantifiable evaluation methodologies
• You track performance across iterations and can explain what makes systems successful
• You prioritize safety, reliability, and user experience alongside capability
• You stay current with the rapidly evolving LLM landscape
• You can quickly adapt to new models, frameworks, and techniques
• You're comfortable working in ambiguous problem spaces and breaking down complex challenges

Benefits

Remote work, flexible hours