Senior AI Engineer

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August 19

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Apollo.io

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B2B • SaaS • Artificial Intelligence

Apollo. io is an all-in-one sales platform designed to streamline and enhance sales operations from lead generation to deal management. The platform offers tools for contact and account search, scores and signals analysis, inbound optimization, sales engagement, and more, leveraging AI and a living data network for comprehensive sales intelligence. Apollo. io is ideal for sales professionals, marketers, and revenue operations teams aiming to enhance productivity and efficiency by automating workflow tasks and integrating CRM systems. With a focus on improving sales performance through analytics and conversation intelligence, Apollo. io helps businesses find the right leads at the right time, nurture those leads effectively, and close deals efficiently.

51 - 200 employees

Founded 2015

🤝 B2B

☁️ SaaS

🤖 Artificial Intelligence

📋 Description

• 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

🎯 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 • 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 • 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