Senior AI Engineer

<h3>📋 Description</h3> • As a Senior AI Engineer on Apollo.io's AI Engineering team, you will be responsible for building and productionizing advanced AI systems powered by LLMs and intelligent agents. • You'll work on capabilities including AI Assistant, Autonomous AI Agents, Deep Research Agents, Conversational Assistant, Semantic Search, Search Personalization, and AI Power Automation. • The mission is to leverage Apollo's scalable data and cutting-edge AI to understand user behavior, personalize experiences, and optimize the customer journey through intelligent automation. • You will design and deploy production LLM systems serving millions of users with high availability and performance. • You will develop agent architectures, context management, backend systems, and AI features like conversational AI and personalized email generation. • Optional focus on Classical AI/ML features like search scoring, entity extraction, Lookalike and recommendation systems <h3>🎯 Requirements</h3> • 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 • 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