Role overview
We're looking for a Senior AI Engineer to help us build the next generation of AI-native products at Awin. The features you'll work on are used directly by advertisers, publishers and partner managers across our global ecosystem to grow and run their affiliate programs things like conversational AI experiences, intelligent automation and decision-support tools built on top of Large Language Models and agent-based architectures.
This is a senior product engineering role where you will own real problems end-to-end, make architecture decisions the team builds on and balance technical depth with the realities of shipping software to customers. We expect you to have an opinion on the system design, the evaluation strategy and the way we operate AI in production.
The Team
You will be joining the AI Solutions team in our Growth Domain. This team is responsible for building AI-native products. Our work is highly visible and every feature you ship can have a real impact on how our customers manage and grow their affiliate programs.
Our engineering culture is built on pragmatic problem solving, strong ownership, fast iteration and a high production bar set through rigorous evaluation, observability and safe-by-default design. We're cloud-first on AWS, working with a Python-based stack that combines agent orchestration, retrieval and standard backend foundations, refreshed as the field moves.
The team practices scrum and you'll have the opportunity to bring new ideas whether that's a different way to solve a problem, an improvement to how we work or sharing best practices for a new technology within the team.
Along with access to learning platforms, hackathons and cross-team initiatives, you will have plenty of opportunities to learn and gain experience from people across the many other teams within Awin.
What you'll work on
- 8+ years of professional software engineering experience, with a track record of shipping production systems.
- Strong Python and backend system design.
- Real, hands-on experience building and running production LLM-based systems not just prototypes.
- Deep experience with agent-based architectures using frameworks such as LangGraph, the Deep Agents or equivalents with multi-step reasoning, tool use, sub-agent orchestration, state and streaming.
- Strong working knowledge of retrieval and context construction (RAG, embeddings, chunking, ranking) and good judgement about when to use those patterns inside an agentic system.
- A solid track record of evaluating and debugging AI systems: structured evals, regression tests and tracing or observability with tools such as LangSmith or similar.
- Familiarity with emerging agent orchestration standards (MCP or similar). Experience with vector databases and hybrid retrieval.
- A clear understanding of LLM failure modes like prompt injection, content safety, context leakage and how to mitigate them in real systems.
- Strong fundamentals in distributed systems, API design and cloud-native architectures on AWS (ECS, Lambda, S3, API Gateway).
- Confident with PostgreSQL, Redis, Docker and modern CI/CD pipelines like GitHub Actions.
- A collaborative engineer who's comfortable working in a Scrum team alongside Product, UX and cross functional teams.
What we're looking for
- Experimentation frameworks (A/B testing, offline-to-online metric correlation).
- Cost-aware AI engineering at production scale in monitoring token usage, model selection, caching and routing strategies that keep latency and spend predictable as traffic grows.