Responsibilities
- Lead full lifecycle development of the agentic platform, transitioning research prototypes into robust, production-ready systems with a focus on long-term workflow orchestration.
- Design and implement scalable, fault-tolerant systems for workflow management, evaluation, and event-driven processing to ensure reliability and performance.
- Establish clear SDK interfaces and API specifications that separate model development from agent outputs, enabling independent iteration while maintaining system stability.
- Develop infrastructure-as-code templates using AWS CDK and Terraform, and convert experimental AI models into standardized, reusable platform components.
- Ensure the platform maintains high efficiency in resource usage and cost while meeting strict performance and accuracy requirements for scientific applications.
- Define and promote best practices for agent architecture, deployment strategies, and code quality to guide platform-wide technical standards.
- Collaborate with modeling, agent, and product teams to coordinate integrations, manage dependencies, and support engineering mentorship across disciplines.
Responsibilities
- Own the agentic platform end to end, from research prototype to production, building the durable orchestration layer for long-running, multi-stage scientific workflows.
- Architect workflow orchestration, evaluation frameworks, and event-driven distributed systems that deliver scale, resilience, and reliability.
- Define SDK abstractions and API contracts between model and agent outputs and downstream consumers, decoupling rapid model iteration from product stability and reducing integration friction across teams.
- Deliver infrastructure-as-code reference implementations (AWS CDK, Terraform, containerized services) and transform exploratory AI proof-of-concepts into hardened, reusable patterns adopted across product domains.
- Own platform cost-efficiency and scalability while preserving the performance and accuracy that our experiments and end users require.
- Explore and template agent design and deployment patterns, and set technical standards, code quality, and architectural direction for the platform.
- Partner across teams (modeling and agent teams, product) to align on integration points, dependency timelines, and delivery, and mentor engineers and scientists.
