What youll be doing:
Lead the build and delivery of production AI systems across machine learning, computer vision, LLM, and agentic use cases.
Build AI applications and agents that use tools, complete multi-step workflows, maintain state, and operate safely in production.
Develop evaluation strategies, test suites, quality metrics, and production feedback loops for models and AI applications.
Build scalable architectures covering model serving, APIs, data flows, workflow orchestration, observability, security, and failure recovery.
Build durable, distributed workflows using platforms such as Temporal, Prefect, or comparable technologies.
Deploy, monitor, and continuously improve AI systems for quality, latency, efficiency, reliability, scalability, and cost.
Make informed technical decisions around model selection, inference architecture, context management, structured outputs, tool use, and infrastructure.
Establish effective development, deployment, and validation practices for services, models, workflows, and infrastructure And provide technical leadership through architecture reviews, build decisions, code reviews, mentoring, and engineering guidelines.
What we need to see:
5+ years of experience in machine learning engineering, AI engineering, software engineering, platform engineering, or a comparable production-focused role.
Bachelors degree
A solid history of advancing innovative AI or machine learning systems from prototype to production.
Extensive knowledge in one or more fields including classical machine learning, computer vision, NLP, generative AI, or LLM applications.
Strong system-design skills, including experience with distributed systems, data-intensive applications, and cloud infrastructure.
Practical understanding of production LLM inference, including latency and efficiency trade-offs, context windows, token usage, model selection, and cost management.
Experience working with containers, orchestration platforms, CI/CD, monitoring, observability, and production incident investigation.
Sound engineering judgment around scalability, reliability, security, maintainability, and operational complexity.
The ability to independently guide complex technical projects and make effective decisions in ambiguous environments.
Strong communication and collaboration skills, including the ability to explain technical trade-offs to engineers, product teams, customers, and other collaborators.
Ways to stand out from the crowd:
Experience working with both traditional machine learning systems and contemporary LLM or agentic applications.
Excellent judgment about when agent-based approaches are appropriate-and when a simpler solution is more effective.
Experience making AI behavior measurable, observable, explainable, and safe in production.
Experience optimizing inference systems for performance, infrastructure efficiency, and operating cost.
A history of guiding engineers or heading cross-departmental technical projects.
