Design, write, and deploy production-grade code for multi-agent workflows, tool-use orchestration engines, and dynamic decision-making pipelines.
Deploy AI frameworks and tools, to enable velocity in product development and scalability of agentic applications that require high-precision and safe execution under complex real-world conditions.
Build a high-throughput model serving pipelines and real-time execution environments optimized for latency, reliability, and scale. Integrate continuous, data-motivated feedback loops into AI development methodologies. Construct automated testing, evaluation, and observability frameworks to assess agent coverage, monitor regressions, and safeguard product quality across end-to-end workflows.
Adopt, integrate distributed tracing, data lineage architectures, telemetry into high-volume generative AI data pipelines. Partner with product owners, domain experts to translate challenges into AI solutions.
Lead feature design reviews, establish coding standards for agentic pipelines. Write maintainable code (e.g., Python, Go etc) to build robust platform abstractions.
Minimum qualifications
Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Experience with AI/ML technologies or infrastructure.
Experience in building simulation orchestration environments, multi-agent frameworks and tools.
Experience with adopting tracing, logging, and monitoring for agentic workflows.
Preferred qualifications
Master's degree or PhD in Computer Science or a related technical field.
5 years of experience with data structures and algorithms.
1 year of experience in a technical leadership role.
Experience developing accessible technologies.
Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Experience with AI/ML technologies or infrastructure.
Experience in building simulation orchestration environments, multi-agent frameworks and tools.
Experience with adopting tracing, logging, and monitoring for agentic workflows.
