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Role 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

• 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

Qualifications

• 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)

Requirements

• #LI-Remote
• #LI-MK1

Benefits

• The US national base salary range for this full-time position is $133,000 - $235,000.
• Our salary ranges are determined by role, level, and location.
• Individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
• Compensation details listed in US role postings reflect the base salary only and do not include applicable bonus, equity, or benefits.
• Further details of our US benefits can be found at Ethos Careers .