AI & ML Infrastructure Architect

Role overview Design the infrastructure behind production AI features, including language-model workflows, semantic search, and document intelligence. This role combines system architecture and hands-on engineering to make inference and retrieval scalable, efficient, observable, and controlled by reliable quality safeguards.

Responsibilities - Architect agent workflows using orchestration frameworks and custom Python or Rust workers. - Build retrieval-augmented generation and vector search systems with technologies such as Pinecone, Qdrant, or pgvector. - Create evaluation tooling, structured-output validation, and safeguards to detect or limit unreliable model responses. - Reduce inference cost and latency through caching, model tiering, and related optimization techniques. - Partner with product teams on capabilities such as document ingestion, automated triage, and conversational assistants.

Requirements - At least 5 years of software engineering experience, including 2 or more years focused on LLM or AI infrastructure. - Strong Python, Rust, or Go skills and a solid grasp of asynchronous execution. - Production experience with vector search and embedding systems. - Familiarity with current hosted and self-managed model APIs. - Experience with distributed systems, rate limiting, token-use optimization, and AI-service observability.

Nice to have - Fine-tuning open-weight models with LoRA or QLoRA. - Contributions to open-source AI tooling or published work on ML systems. - Experience with edge AI runtimes or WebAssembly inference.

Benefits and work setup - Full-time, globally remote role with flexible, asynchronous work practices. - Compensation is listed at $90,000–$150,000 USD annually plus equity. - The role description mentions health coverage for the employee and direct dependents, and hardware support.