Principal AI Engineer

Overview

Apollo.io is the leading go-to-market solution for revenue teams, trusted by over 500,000 companies and millions of users globally, from rapidly growing startups to some of the world's largest enterprises. Our platform helps sales and marketing teams prospect smarter, automate workflows, and close more deals, all within a unified, AI-native platform.

Backed by world-class investors including Sequoia and Bain Capital, Apollo is one of the fastest-growing SaaS companies in the world, valued at $1.6B following our Series D.

We are entering a new phase of AI-native growth and are looking for a Principal AI Engineer to lead the design and deployment of cutting-edge agentic systems, AI assistants, and LLM-powered features. This role is pivotal to Apollo’s AI roadmap
, owning the architecture and productionization of intelligent systems that power the workflows of thousands of GTM teams globally.

You’ll be a technical thought leader on the AI Engineering team and work alongside product, backend, and platform leaders to drive forward Apollo’s competitive edge in applied AI.

• Autonomous AI Agents
  :
  Architect and lead the development of multi-agent systems capable of long-horizon planning, reasoning, and API orchestration.
• Workflow Automation
  :
  Build reusable agentic components that integrate deeply into sales and marketing processes.
• LLM Platformization
  :
  Own and evolve our in-house platform for scalable, low-latency, and cost-efficient LLM and agent deployments.

• Conversational AI & UI
  :
  Lead design of interfaces powered by natural language understanding and retrieval-augmented generation (RAG).
• Semantic & Personalized Search
  :
  Build embedding-based, intent-aware search and personalization systems tuned to business user needs.
• Email Intelligence
  :
  Drive innovation in personalized outreach generation using context-aware generation pipelines.

• Latency & Cost Optimization
  :
  Tune inference pipelines, caching layers, and model selection logic for high-scale, cost-aware performance.
• Evaluation at Scale
  :
  Define and drive robust offline and online testing methodologies (A/B, sandboxing, human evals) across agents and LLM flows.
• Feedback Loops
  :
  Architect human-in-the-loop systems and telemetry to improve accuracy, UX, and explainability over time.

• Technical & Production Depth
  : 10+ years of software engineering experience, with at least 3 years in applied LLM or agentic AI systems (2023–present).
• Proven success in deploying LLM-powered products
  :
  Used by real users at scale, not just prototypes or internal tools.
• Deep backend & systems engineering
  :
  Expertise with Python, distributed systems, and scalable APIs.
• Familiarity with Lang Chain, Llama Index, or similar orchestration frameworks
  .
• Experience with RAG pipelines, vector DBs, embedding models, and semantic search tuning.
• Experience managing performance across cloud providers (e.g., AWS Bedrock, OpenAI, Anthropic, etc.).

• Demonstrated experience building multi-step agents
  , planning workflows, chaining reasoning steps, and integrating APIs with agent memory/state.
• Comfort with advanced prompting strategies
  , few-shot and chain-of-thought reasoning, and embedding retrieval setups.

• Strong understanding of AI system evaluation
  , human ratings, A/B experimentation, and feedback loop pipelines.
• Experience designing safety-aware, reliable LLM systems in production environments.
• Experience owning logging, monitoring, and observability for live AI systems.

• Principal-Level Ownership
  :
  You thrive in an ambiguous environment, define company-wide roadmaps, drive the most important Engineering decisions, and mentor others. You lead from the front.
• AI-Native Mentality
  :
  You leverage AI to ship faster and smarter, and champion automation across engineering workflows.
• Applied Focus
  :
  You prioritize impact over novelty. You’re deeply pragmatic in your application of AI research to product features.

A Role with Real Autonomy
:
You’ll own complex systems end-to-end,…