Senior AI Engineer
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Requirements
- 7+ years building production systems; 2+ years hands-on LLMs/RAG
- 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)
- #LI-Remote #LI-MK1
- #LI-Remote
- #LI-MK1
- The US national base salary range for this full-time position is $146,000 - $236,000. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
- Please note that the compensation details listed in US role postings reflect the base salary only and do not include applicable bonus, equity, or benefits.
- You can find further details of our US benefits at https://www.ethoslife.com/careers/
- Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. At Ethos we are dedicated to building a diverse, inclusive and authentic workplace.
Responsibilities
- Production RAG: indexing, retrieval, hybrid search, reranking, query rewriting, grounding, citations
- Production RAG
- Context Graph: entity resolution + linking + provenance; graph + vector retrieval; supports multi-hop context
- Context Graph
- LLM orchestration: tool/function calling, structured outputs, routing across model tiers, failure modes
- LLM orchestration
- GPU/inference cost optimization: batching, caching/KV reuse, quantization, autoscaling; optimize $/session + latency
- GPU/inference cost optimization
- Safety + compliance: PII/PHI handling, redaction, audit logs, deterministic replay, hallucination mitigation
- Safety + compliance
- LLMOps: eval harness (golden sets, regression, adversarial), monitoring for quality/cost/drift
- LLMOps
- 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
Benefits
- Full stack technology platform as the backbone of family financial health.
- Access to cutting-edge deep technology and data science tools for streamlining processes.
- Opportunity to work on LLM powered copilots across critical workflows, enhancing productivity in underwriting, agent enablement, customer support, operations/compliance, fraud detection.
- Responsibilities include improving quality and trust via evaluation, feedback loops, evidence-backed outputs.
- Partner with various teams to raise engineering standards; write clear design docs.
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