Job Opportunities API

You believe great ML systems don't just work — they scale, they recover gracefully, and they give data scientists the confidence to iterate quickly. At Expel, you'll be a key contributor in building and maturing the infrastructure that powers our machine learning and generative AI capabilities. From end-to-end training pipelines to the specialized infrastructure behind production agentic applications, your work will directly shape how fast we can innovate and how reliably our AI systems run. You'll work closely with senior and principal engineers, data scientists, and cross-functional teams to operationalize ML at scale. You bring strong hands-on expertise and a genuine drive to continuously improve the systems and practices around you. What Expel can do for you Give you hard, meaningful problems — building the infrastructure that lets defenders win using AI Connect you with a collaborative team of engineers, data scientists, and researchers who care about doing it right Offer unlimited PTO (that leadership models and encourages), up to 24 weeks of parental leave, and really excellent health benefits Pay you a monthly fitness and cell phone stipends — no receipts required Support your professional growth with a conference benefit and continuous learning opportunities Offer full remote flexibility — work from wherever you do your best work What you can do for Expel Build and scale ML infrastructure Architect and maintain end-to-end machine learning training pipelines on AWS (SageMaker, EKS, Step Functions) to ensure reliable and reproducible model development and deployment Build and maintain infrastructure for production agentic applications using Amazon Bedrock and Bedrock AgentCore — including agent runtimes, memory, secure gateways, and observability at scale Contribute to the architectural evolution of our ML platform, including evaluating MLOps tooling and participating in buy vs. build decisions Operationalize with rigor Implement AI/ML governance best practices for model versioning, testing, validation, maintenance, and security Integrate MLOps best practices with Expel's SDLC, security, and infrastructure standards, working alongside SRE, Platform Engineering, and Security teams Drive quality, reliability, and scalability improvements through thoughtful engineering and monitoring Collaborate and enable Partner with data scientists, software engineers, and stakeholders to operationalize ML models reliably and at scale Mentor and support junior engineers; foster a culture of engineering excellence Create and maintain documentation, internal tooling, and enablement resources so practitioners across Expel can work effectively with ML systems Stay current with the MLOps landscape and bring relevant innovations back to the team What you should bring with you Collaboration & communication Clear communicator — able to write documentation and explain technical concepts to both engineering and non-technical audiences Strong collaborator with engineers, product managers, and business stakeholders Demonstrated ability to mentor others and invest in the growth of the people around you Balances near-term delivery with longer-term technical quality Technical depth Strong Python proficiency; familiarity with other languages (Go, JS) is a plus Solid experience with CI/CD pipelines, infrastructure-as-code, and containerization for ML workloads Hands-on experience with cloud-based ML platforms — AWS (SageMaker, Bedrock, Bedrock AgentCore) strongly preferred; GCP (Vertex AI) experience also valued Proven experience operationalizing LLMs and building infrastructure for complex agentic applications — agent orchestration, memory, tool calling, RAG architectures Familiarity with ML frameworks including Scikit-Learn, PyTorch, Spark, and TensorFlow Working knowledge of continuous retraining, concept drift m