[Hiring] Senior AI Engineer @pubx

Role Description

PubX builds next-generation agentic advertising infrastructure. Our AI makes real-time, revenue-critical decisions for digital publishers and advertisers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers.

As a founding member of AgenticAdvertising.org, we're building the next generation of autonomous advertising infrastructure.

Who You Are

• A pragmatic engineering mindset: you know when to use an agent framework, when a simpler workflow is better, and how to balance model capability, reliability, latency, cost, and user experience.
• Communicate technical ideas well in writing and conversation to both technical and non-technical audiences.
• Write clean, well-tested code with thoughtful abstractions that’s easy to extend and operate.
• Learn quickly when things are unfamiliar by prototyping, then hardening and documenting what you ship.

What You’ll Work On

• Design, build, and maintain backend services and APIs across REST, GraphQL, and gRPC that power agentic AI features and core product workflows.
• Build production-grade AI agent systems using modern frameworks and patterns such as LangChain, LangGraph, tool/function calling, structured outputs, multi-step planning, stateful agent sessions, human-in-the-loop workflows, and agent memory.
• Design and integrate agent interoperability patterns, including Model Context Protocol (MCP) for tool and data access, and Agent-to-Agent (A2A) communication patterns for multi-agent collaboration.
• Develop web applications that integrate tightly with ML and LLM-driven systems, including stateful conversations, retries, fallbacks, and explainability surfaces.
• Build and operate event-driven components using Kafka and message queues such as SQS or RabbitMQ, designing asynchronous workflows for long-running agent jobs, background reasoning tasks, tool execution, and orchestration pipelines.
• Implement observability, evaluation, and debugging workflows for AI systems using tools such as Langfuse, LangSmith, OpenTelemetry, structured logs, metrics, tracing, agent/tool traces, dashboards, alerting, and post-incident reviews.
• Deploy and operate services on AWS, including ECS, EKS, Lambda, API Gateway, ALB, RDS, DynamoDB, S3, and related services, using infrastructure-as-code and secure-by-default engineering patterns.
• Continuously improve reliability, performance, cost efficiency, developer experience, and production-readiness across both traditional backend systems and AI-native workflows.

What We’re Looking For

• Experienced engineer who has built production systems and enjoys solving practical problems with AI.
• Comfortable working across backend engineering, cloud infrastructure, and LLM-powered product experiences.
• Strong backend engineering fundamentals, including API-first design, data modeling, distributed systems, performance, security, authentication, authorization, and production operations.
• Hands-on experience integrating LLMs or ML components into real product systems, including streaming responses, async jobs, state management, caching, retrieval, grounding, evaluations, and monitoring.
• Practical experience with agentic AI frameworks and patterns, such as LangChain, LangGraph, LlamaIndex, OpenAI/Anthropic APIs, tool calling, structured outputs, agent routing, multi-agent workflows, and human-in-the-loop systems.
• Experience with LLM observability and evaluation workflows, ideally using tools such as Langfuse, LangSmith, OpenTelemetry, custom evaluation harnesses, prompt/version management, datasets, regression testing, and LLM-as-judge approaches.
• Strong hands-on experience with AI-assisted development workflows in a real codebase, not just demos.
• Experience working with distributed systems with event-driven patterns, including Kafka, SQS, RabbitMQ, pub/sub architectures, background workers, retries, dead-letter queues, idempotency, and workflow orchestration.
• Hands-on AWS experience with production operations, monitoring, incident response, infrastructure-as-code, cost awareness, and secure deployment practices.

Nice to Have

• Experience with MCP servers or clients, A2A-style agent communication, tool registries, agent authentication, permissions, audit logging, and secure tool execution.
• Experience with retrieval-augmented generation systems, vector databases, embedding pipelines, reranking, hybrid search, citation generation, and grounding quality evaluation.
• Experience designing safeguards for LLM systems, including prompt-injection mitigation, output validation, PII handling, policy enforcement, sandboxing, rate limiting, and abuse monitoring.
• Experience with modern AI developer tooling such as Cursor, Claude Code, GitHub Copilot, OpenAI Codex, or similar coding-agent workflows in production engineering teams.

Who This Role Will Suit

• Engineers who like a mix of autonomy and collaboration, and who are comfortable working in an environment that’s still evolving.
• Comfort with async collaboration and clear written communication is important.
• Use of agentic coding tools heavily (e.g. Cursor and Claude Code) to plan, scaffold, refactor, and debug production code.

Company Benefits

• Competitive salary with meaningful equity
• Fully remote, async-friendly working
• Supportive, low-ego engineering culture
• Budget for learning and professional development

Interview Process

• CV & Profile Review – Relevant experience and background
• Initial Chat (30 mins) – Motivation and role fit
• Architecture Interview (60 mins) – Architecture, design choices, and real scenarios
• Agentic Coding Exercise (60 mins) – A small task related to the role, executed by agents

If you’re interested in building and shaping real systems in a growing product company, at the forefront of AdTech innovation, we’d love to hear from you.

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