Senior ML Engineer; Agentic AI

PubX builds next-generation publisher-first agentic advertising infrastructure. Our AI makes real-time, revenue-critical pricing decisions for digital publishers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers. We’re currently ranked #5 globally in Prebid Analytics Adapter Rankings, and growing.

Digital publishers leave significant revenue on the table because ad pricing is still largely manual, static or simple rule-based. Every ad impression is unique, but most pricing systems treat them the same. PubX's AI analyzes bid-stream data and historical patterns to arrange optimal deals, in near real-time.

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

• Design, train, evaluate, and deploy ML models that power real-time pricing decisions across billions of ad auction events
• Build and maintain production ML pipelines end-to-end: feature engineering, training, validation, deployment, monitoring, and retraining
• Develop and operate MLOps infrastructure (experiment tracking, model registry, A/B testing frameworks, automated retraining) on AWS using infrastructure-as-code
• Integrate LLM and agentic AI components into product workflows, including prompt engineering, orchestration, evaluation, and feedback loops
• Build feature pipelines and serving layers that operate at low latency and high throughput, working closely with data and backend engineers
• Own model observability and reliability: drift detection, performance monitoring, alerting, SLAs, and post-incident reviews
• Contribute to backend services and data pipelines where needed to close the gap between model development and production delivery

We’re looking for an experienced engineer who has worked on production systems and enjoys solving practical problems with AI.

You’ve likely have:

• Strong ML engineering fundamentals: model development, feature engineering, evaluation methodology, and a solid understanding of when (and when not) to apply ML
• Hands‑on experience deploying and operating ML models in production, including managing model lifecycle, versioning, A/B testing, and monitoring for drift and degradation
• Experience building MLOps tooling and infrastructure (e.g., MLflow, W&B, Sage Maker Pipelines, Kubeflow, or similar) to support reproducible, automated workflows
• Solid Python skills with comfort across the ML ecosystem (PyTorch/Tensor Flow, scikit‑learn, pandas, Spark) and the ability to write production‑quality code — not just notebooks
• Experience integrating LLM/agentic AI components into production systems, including evaluation, grounding, and feedback capture
• Working knowledge of backend engineering: APIs, async processing, and containerised deployments — enough to ship models as reliable services

You tend to:

• Make pragmatic decisions balancing speed, quality, cost, and risk trade‑offs.
• 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.

• Experience with AdTech or other high volume real‑time systems

This role suits engineers who like a mix of autonomy and collaboration, and who are comfortable working in an environment that’s still evolving.

We’re a distributed team with a growing engineering presence in India, so comfort with async collaboration and clear written communication is important.

We use agentic coding tools heavily (e.g. Cursorand Claude Code) to plan, scaffold, refactor, and debug production code, while maintaining strong engineering judgment and ownership of outcomes.

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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