Lead Software Engineer

Job Requirements

Build production AI, agent, data-processing, and platform capabilities across Keystone. The ideal candidate is a hands-on engineer who can develop Python-based AI applications and agents, integrate LLMs and tools into production workflows, and work comfortably with event driven and data intensive systems.

Build production AI agents, AI services, data workflows, integrations, and platform components

Design and develop production AI agents and multi-step agent workflows using Python and Embabel.

Build agent capabilities including tool calling, orchestration, planning, workflow execution, and integration with platform services.Integrate LLMs and AI services into production applications with appropriate reliability, observability, and cost controls.Develop knowledge and retrieval workflows, including RAG-style patterns, search, contextual data retrieval, and grounded AI responses where applicable.Build and maintain event-driven services using Kafka and related technologies.

Develop producers, consumers, integrations, retry mechanisms, and failure-handling workflows.Implement governed data contracts using Schema Registry and Avro or Protobuf.Develop data-processing workflows for ingestion, enrichment, validation, and publication.Build APIs and integrations connecting AI agents with internal services, data sources, and external platforms.Implement reliable and idempotent processing, deduplication, validation, and error handling.

Build automated tests and evaluation workflows for AI and platform components.

Participate in code reviews, incident response, troubleshooting, and operational ownership.

Use CI/CD, Docker, AWS/cloud infrastructure, and infrastructure-as-code practices to deploy and operate services.Strong production experience with Python and backend/service developmentHands-on experience building AI applications, LLM-powered systems, or production agent workflows.

Experience with agent development/orchestration frameworks; Embabel experience is highly relevant.

Understanding of LLMs, prompting, tool calling, structured outputs, retrieval/RAG, and AI application patterns.

Practical experience with Kafka, including partitions, offsets, consumer groups, reprocessing, retention, ordering, and failure handling.Strong SQL and data modelling experience.Experience with at least one data platform or lakehouse technology such

Work Experience

7-10Years