# EnFi — Senior AI Engineer

- Generated: 2026-09-02 05:35:09 PM EDT
- Original JD posting: https://venturefizz.com/job/senior-ai-engineer/
- Work model/location: Not disclosed on the canonical listing
- Compensation: Not disclosed
- Travel: Not disclosed
- Posting time: Not disclosed
- Applicants: Not disclosed
- Positioning track: Technical IC
- Fit outcome: FAIL — 72%

## Fit decision

FAIL. Keith strongly matches the AI-system mandate, but the posting explicitly requires strong proficiency across TypeScript, React, Go, and Python; current evidence does not support strong TypeScript or Go proficiency. The role-specific work model also remains unverifiable.

## Direct-match strengths

1. Direct production LLM, RAG, agents, tool orchestration, APIs, ingestion, vector/graph database, evaluation, and monitoring experience.
2. Strong experience translating ambiguous enterprise workflows into production AI systems.
3. Direct knowledge-graph, Neptune, regulated-domain, explainability, cloud, and distributed-system evidence.

## Hard/material gaps and caveats

1. Hard required-stack gap: strong TypeScript and Go proficiency is not documented.
2. Work model/location is not disclosed after reasonable inspection.
3. Compensation is not disclosed.
4. Material overqualification risk for a senior IC builder role.

## Full normalized job description

EnFi seeks a Senior AI Engineer to own production AI systems end to end. Responsibilities include LLM applications, retrieval and agentic workflows, structured/unstructured data extraction, multi-step tool-calling agents, ingestion and enrichment pipelines, backend/API and user-workflow integration, evaluation and monitoring, product/design/domain collaboration, and scalable/explainable architecture. Required qualifications include 5+ years in software engineering, 3+ years building production AI/ML, hands-on LLM prompting/RAG/tool use/orchestration, strong proficiency in TypeScript, React, Go, Python and modern AI frameworks, vector or graph databases, APIs, distributed systems, cloud infrastructure, and production debugging. Preferred areas include knowledge graphs, Neo4j/Neptune/RDF, entity resolution, structured knowledge plus LLMs, regulated domains, and AI quality metrics.

## Artifact status

- Resume: Not generated (FAIL)
- Cover letter: Not generated (FAIL)
- Google Drive used: No