Software Engineer, Agentic AI

Role overview

Join a forward-deployed engineering team that builds and ships production machine learning systems for enterprise clients. The role blends hands-on model development with backend engineering, cloud infrastructure, and direct client collaboration to solve complex, real-world AI problems.

Responsibilities

- Build and maintain scalable backend systems and data pipelines that power machine learning operations and client-facing deployments. - Manage and optimize cloud infrastructure to ensure reliable deployment and operation of ML models in production. - Independently investigate complex problem spaces and improve system capabilities and performance with minimal guidance. - Partner closely with ML engineers and data scientists to integrate advanced ML technologies across platforms. - Work embedded with client teams to support use case discovery, product development, and AI deployment. - Contribute to research and development of new tools that enhance AI capabilities and workflows.

Requirements

- Two or more years of software engineering experience with a strong focus on ML engineering and deploying machine learning models in production. - Extensive full-stack development experience, particularly in backend environments supporting AI/ML workloads. - Prior experience working directly with clients on use case discovery, product development, and leading client engagements. - Strong proficiency in Python with deep expertise in large language models, AI agents, and ML model development. - Experience designing and deploying scalable ML systems such as retrieval-augmented generation (RAG) pipelines and production-grade AI applications. - Hands-on experience with cloud platforms such as AWS, GCP, or Azure and operational best practices for ML workloads. - Familiarity with Kubernetes and other container management tools. - Ability to write well-structured code with automated unit and end-to-end tests. - Comfort with polyglot persistence models spanning SQL and NoSQL databases. - Experience with MLOps frameworks and DevOps principles as applied to machine learning models.

Nice to have

- Comfort thriving in fast-paced, ambiguous environments and turning open-ended problems into shipped solutions. - Interest in advancing agentic AI systems where autonomous software agents take on meaningful production tasks.