Senior Applied AI Engineer

• 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, devising A/B experiments, 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

• 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

Weekday AI