About the role
This Software Engineer role in the Acceleration Platform team focuses on building scalable, AI-native agentic systems to automate complex developer workflows. The position involves architecting fault-tolerant systems, blending traditional distributed systems with LLM orchestration, and establishing technical strategies for AI safety and performance at an enterprise scale.
TechnologyOnsite
Key Responsibilities
- Architect agentic ecosystems by leading the design and implementation of highly scalable, fault-tolerant systems where multi-agent networks reason, plan, and execute complex workflows across vast, distributed codebases
- Pioneer AI-first engineering by defining best practices for the team and broader organization
- Blend traditional distributed systems architecture with advanced Large Language Model (LLM) orchestration, complex Retrieval Augmented Generation (RAG) pipelines, and optimization
- Scale evaluations and guardrails by establishing a comprehensive technical strategy for AI safety
- Architect automated frameworks that measure performance and enforce security to mitigate across large-scale deployments
- Solve the hardest AI problems through managing the most intricate non-deterministic edge cases
- Build advanced telemetry and introspection tooling that allows the entire organization to understand, debug, and optimize self-supporting behavior
Requirements
- Bachelor's degree or equivalent practical experience
- 1 year of experience with software programming in Python or C++
- 1 year of experience with data structures and algorithms
- 1 year of experience implementing core Machine Learning (ML) concepts
- Experience in AI safety, enterprise security, advanced prompt engineering, and scalable model evaluation methodologies
- Expertise in distributed systems architecture and core programming, paired with a sophisticated, nuanced understanding of LLM capabilities, limitations, and failure modes
- A proven track record of designing, deploying, and scaling LLM-backed applications, complex RAG systems, or self-supporting agents in enterprise production environments