Principal Agentic AI Engineer, Zeta Global

• 10+ years building production backend and/or applied AI systems (design → ship → operate)
• Strong engineering in Java or Go (Python OK for evals/tooling)
• Hands-on experience with LLMs and agentic patterns (tool use, structured outputs, multi-step loops)
• Hands-on RAG experience (embeddings, hybrid retrieval, reranking, chunking, context assembly)
• Experience building evals and monitoring for AI systems (offline benchmarks + online experiments)
• Cloud + distributed systems fundamentals (APIs, microservices, streaming/eventing; AWS preferred)

Desirable

• We’re hiring a hands-on Agentic AI engineer to build bidder-adjacent agents that improve campaign performance using RAG + tool-using workflows in a closed-loop ACE (Observe → Reason → Act → Evaluate)
• This is not a classic “train a model and deploy it” role—this is agentic decisioning + evaluation + safety in a low-latency AdTech environment
• Build bidder-adjacent agentic workflows that recommend/execute campaign control actions (targeting constraints, budget & pacing levers, bid modifiers, supply/inventory selection, creative routing)
• Implement production-grade RAG: retrieval from policies/playbooks, campaign history, aggregates, and near-real-time telemetry; optimize grounding and reduce hallucinations
• Create safe tool/action interfaces: idempotent execution, audit logs, dry-run + approval gates, rate limits, rollback/fallback behaviors
• Own AgentOps: eval harnesses, regression suites, online experimentation (A/B), metrics tied to outcomes (CPA/ROAS, pacing, quality, margin)
• Add observability end-to-end (tracing prompts/retrieval/tool calls/latency) and reliability patterns (timeouts, circuit breakers, safe defaults)
• Partner with Backend/Bidding, Data Platform, DS/Optimization, Product, and SRE to define the boundary between deterministic per-request bidding and agent-driven control-plane decisions