Apollo.io
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.
Responsibilities
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 and 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. AI Assistant and Agent Systems
Agent Architecture and 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 models and agentic platforms that enable widespread adoption and viability of agent development within the Apollo ecosystem. Conversational AI: Build natural language interfaces that understand user intent and provide actionable insights. Natural Language Search: Enhance our semantic search capabilities to understand complex business queries. Personalized Email Generation: Build systems that craft highly personalized outreach messages at scale. Classical AI/ML (Optional Focus)
Search Scoring and Ranking: Develop and improve recommendation systems and search relevance algorithms. Entity Extraction: Build models for automatic company keywords, people keywords, and industry classification. Lookalike and Recommendation Systems: Create intelligent matching and suggestion engines. Requirements
Core AI/LLM Experience (Must-Have). 8+ years of software engineering experience with a focus on production systems. 2+ 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. Documentation-First Approach: Loves to scale up by writing things down to share knowledge asynchronously. Excellent Communication: Able to work with stakeholders to develop key business questions and build systems that answer them. Ambiguity Resolution: Able to break down ambiguous problems into simpler milestones and guide implementation. Self-Motivated and Self-Directed: Thrives in autonomous environments with clear outcomes. Inquisitive Nature: Asks the right questions and digs deeper into problems. Attention to Detail: Organized, diligently approach to complex system development. Integrity: Acts with the utmost integrity in all interactions. Continuous Learning: Genuinely curious and open to learning in a fast-evolving field. Critical Thinking: Proven problem-solving skills and analytical thinking. 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 and 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 and 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. 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.
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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 and 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. AI Assistant and Agent Systems
Agent Architecture and 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 models and agentic platforms that enable widespread adoption and viability of agent development within the Apollo ecosystem. Conversational AI: Build natural language interfaces that understand user intent and provide actionable insights. Natural Language Search: Enhance our semantic search capabilities to understand complex business queries. Personalized Email Generation: Build systems that craft highly personalized outreach messages at scale. Classical AI/ML (Optional Focus)
Search Scoring and Ranking: Develop and improve recommendation systems and search relevance algorithms. Entity Extraction: Build models for automatic company keywords, people keywords, and industry classification. Lookalike and Recommendation Systems: Create intelligent matching and suggestion engines. Requirements
Core AI/LLM Experience (Must-Have). 8+ years of software engineering experience with a focus on production systems. 2+ 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. Documentation-First Approach: Loves to scale up by writing things down to share knowledge asynchronously. Excellent Communication: Able to work with stakeholders to develop key business questions and build systems that answer them. Ambiguity Resolution: Able to break down ambiguous problems into simpler milestones and guide implementation. Self-Motivated and Self-Directed: Thrives in autonomous environments with clear outcomes. Inquisitive Nature: Asks the right questions and digs deeper into problems. Attention to Detail: Organized, diligently approach to complex system development. Integrity: Acts with the utmost integrity in all interactions. Continuous Learning: Genuinely curious and open to learning in a fast-evolving field. Critical Thinking: Proven problem-solving skills and analytical thinking. 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 and 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 and 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. 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.
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