gem
Senior AI Developer
Cadstrom
Remote, CAN
Full-time
Senior · 3+ years experience
Remote
Discovered 3 weeks ago
TypeScriptLLMsRAGLangSmithLangFuseDSPy
Free
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Key Skills for This Role
TypeScriptLLMsRAGLangSmithLangFuseDSPy
Full Job Posting
- Manage highly automated AI flows for data extraction from electrical component datasheets.
- Build AI pipelines that perform validations on customer circuits, integrating retrieval, agentic systems that think like a seasoned EE, and deterministic checks into trustworthy end-to-end flows.
- Build benchmarking pipelines and evaluation tooling that electrical engineering domain experts use to analyze, calibrate, and improve our AI systems.
- Design and ship self-improving systems exposed to our customers – closing the loop between production signal, evaluation, and model and prompt improvements.
- Build and optimize tools (MCP-based or otherwise) for AI agents, ensuring agents can reliably interact with Cadstrom's platform and data.
- Diagnose failure modes by digging into model outputs and traces, documenting root causes, and proposing fixes at the data, prompt, or schema level.
- Partner closely with electrical engineers to internalize domain conventions and translate that understanding into better prompts, schemas, and evaluations.
- 3-4 years of hands-on experience in AI/ML or applied data engineering with shipped work in production or active pilot.
- Practical experience with LLMs (prompt engineering, few-shot/ICL, RAG, or agentic patterns) and interest in multi-modal inputs (PDFs, tables, figures).
- TypeScript experience – typing, packaging, services, and automated tests.
- Strong grasp of experiment design: reproducibility, significance, appropriate metrics, and ground-truth curation.
- Git proficiency and collaborative dev habits (peer reviews, readable self-documenting code).
- Meticulous attention to detail in debugging and root-cause analysis.
- Excellent written and visual communication; you summarize messy problems crisply and use plots and figures to convey insights.
- Undergraduate degree in Computer Science, Engineering, or a related STEM field.
- Experience building and optimizing tools (MCP-based or otherwise) for use by AI agents.
- Electronics fundamentals (e.g. interpreting a datasheet) via coursework or hobbyist experience.
- Retrieval (vector DBs), chunking/embedding strategies, and context budgeting.
- LLM-ops platforms (LangSmith, LangFuse, DSPy).
- Data pipelines in a cloud environment (AWS/GCP/Azure).
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