Senior AI Engineer

Own your future:

Our culture isn't something people join, it's something they build and shape. We believe that every person deserves to be heard and empowered. If you're on the fence about whether you're a fit, we say go for it. Let’s build something great together.

As a Senior AI Engineer, you will design and deliver production-ready AI systems that power intelligent product experiences across the platform. This role combines strong applied AI engineering skills with solid software engineering fundamentals, with a focus on implementation quality, scalability, observability, and reliability in production.

You will collaborate across product, engineering, and data teams to bring LLM-powered features, retrieval systems, and agentic workflows into production, while contributing to strong engineering practices and high-quality delivery across the team.

Must haves

- Proven experience building and shipping customer-facing AI systems in production
- Strong experience with Generative AI and LLMs, including RAG, evaluation, orchestration, and cost/latency optimization
- Experience building agentic or multi-step AI systems with tools, retrieval, and external integrations
- Strong programming skills in Python; additional experience with Go, Java, or similar languages will be a plus
- Strong software engineering fundamentals in testing, observability, reliability, and production support
- Ability to independently drive technical solutions from design through implementation and production rollout

Nice to have

- Experience with AWS services (e.g., EC2, EKS, RDS, ELB), infrastructure as code (Terraform), and CI/CD setup
- Strong experience with LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks
- Strong experience with AWS Bedrock, SageMaker, or similar managed AI platforms
- Experience with vector databases, embeddings, semantic search, and retrieval design
- Experience mentoring engineers or owning technically complex projects

Key responsibilities

- Design, build, and operate AI systems and services for customer-facing products
- Implement LLM pipelines, retrieval systems, and agentic workflows in production
- Partner with product and engineering teams to turn user and business needs into shipped AI features
- Improve observability, performance, reliability, and cost efficiency of AI systems in production
- Contribute reusable components, engineering practices, and implementation quality across the team
- Support evaluation, testing, and iterative improvement of AI-powered product capabilities
- Review code, support quality initiatives, and contribute to operational excellence
- Troubleshoot production issues and participate in on-call support rotations

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