Senior AI Engineer, Apollo.io

• 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
• Production LLM Applications: Demonstrated experience building customer-facing, scalable LLM-powered products with real user usage (not just POCs or internal tools)
• Agent Development: Experience building multi-step AI agents, LLM chaining, and complex workflow automation
• Prompt Engineering Expertise: Deep understanding of prompting strategies, few-shot learning, chain-of-thought reasoning, and prompt optimization techniques
• Python Proficiency: Expert-level Python skills for production AI systems
• Backend Engineering: Strong experience building scalable backend systems, APIs, and distributed architectures
• LangChain or Similar Frameworks: Experience with LangChain, LlamaIndex, or other LLM application frameworks
• API Integration: Proven ability to integrate multiple APIs and services to create advanced AI capabilities
• Production Deployment: Experience deploying and managing AI models in cloud environments (AWS, GCP, Azure)
• Testing & Evaluation: Experience implementing rigorous evaluation frameworks for LLM systems including accuracy, safety, and performance metrics
• A/B Testing: Understanding of experimental design for AI system optimization
• Monitoring & Reliability: Experience with production monitoring, alerting, and debugging complex AI systems
• Data Pipeline Management: Experience building and maintaining scalable data pipelines that power AI systems

Desirable

• As a Senior AI Engineer on our AI Engineering team, you will be responsible for building and productionizing advanced AI systems powered by Large Language Models (LLMs) and intelligent agents
• You'll work on critical Apollo capabilities including our AI Assistant, Autonomous AI Agents, Deep Research Agents, Conversational Assistant, Semantic Search, Search Personalization, and AI Power Automation features that directly impact millions of users' productivity
• The mission of our AI teams is to leverage Apollo's massive scale data and cutting-edge AI to understand and predict user behaviors, personalize experiences, and optimize every stage of the customer journey through intelligent automation
• Agent Architecture & Implementation: Build sophisticated multi-agent systems that can reason, plan, and execute complex sales workflows
• Context Management: Develop systems that maintain conversational context across complex multi-turn interactions
• LLM and Agentic Platforms: Build scalable large language model and agentic platforms that enable widespread adoption and viability of agent development within the Apollo ecosystem
• Backend Systems: Build back-end systems necessary to support the agents
• AI features: Conversational AI, Natural Language Search, Personalized Email Generation and similar AI features
• Search Scoring & Ranking: Develop and improve recommendation systems and search relevance algorithms
• Entity Extraction: Build models for automatic company keywords, people keywords, and industry classification
• Lookalike & Recommendation Systems: 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