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Ethos Logo Ethos · IT - Software Development

Senior AI Engineer

United States 146000.00 - 236000.00 USD / Year · Job Posted January 29, 2026
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Job Description

We’re building several LLM-powered copilots across critical workflows (e.g., underwriting productivity, agent enablement, customer support, operations/compliance, fraud). We need an AI engineer to own the LLM + retrieval + context layer that makes these copilots accurate, auditable, fast, and cost-efficient. Typical stack: Python/FastAPI, Postgres + vector (pgvector/Pinecone/Weaviate), OpenSearch, optional graph DB, Kubernetes + GPUs, OTEL/Datadog

Job Responsibility

  • Production RAG: indexing, retrieval, hybrid search, reranking, query rewriting, grounding, citations
  • Context Graph: entity resolution + linking + provenance
  • graph + vector retrieval
  • supports multi-hop context
  • LLM orchestration: tool/function calling, structured outputs, routing across model tiers, failure modes
  • GPU/inference cost optimization: batching, caching/KV reuse, quantization, autoscaling
  • optimize $/session + latency
  • Safety + compliance: PII/PHI handling, redaction, audit logs, deterministic replay, hallucination mitigation
  • LLMOps: eval harness (golden sets, regression, adversarial), monitoring for quality/cost/drift
  • Design/ship the end-to-end pipeline: retrieve → assemble context → generate → cite → log/monitor
  • Improve quality and trust via evaluation, feedback loops, and clear evidence-backed outputs
  • Partner with product, security, and domain teams
  • write crisp design docs
  • raise engineering bar
  • Ship RAG v1 with citations + measurable quality metrics
  • Deliver Context Graph v1 that improves retrieval on real copilot tasks
  • Reduce cost/latency with a concrete inference optimization plan shipped to prod

Requirements

  • 7+ years building production systems
  • 2+ years hands-on LLMs/RAG
  • Proven RAG experience (embeddings, vector DBs, hybrid search, reranking, eval)
  • Strong backend/distributed systems + observability
  • Track record shipping in high-stakes environments with auditability/correctness
  • Knowledge graph / entity resolution / provenance systems
  • GPU inference optimization (vLLM/TGI/TensorRT-LLM, quantization AWQ/GPTQ, batching)
  • Regulated domain experience (insurance/fintech/healthcare)
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