# Software Engineer, Artificial Intelligence/LLM

**Company:** [Beacon AI](https://hotfix.jobs/companies/beacon-ai)
**Location:** San Carlos, CA
**Role:** ML Engineering
**Salary:** $135k – $260k/yr
**Experience:** 5+ years
**Skills:** LLMs, RAG, LangChain, Python, TypeScript, Aws Bedrock, OpenAI, Anthropic, Embeddings, Vector Search, Opensearch, Pgvector, Pinecone, Evaluation Frameworks, Prompt Engineering
**Posted:** 2026-07-15

> Build and ship production LLM-powered features for aviation safety platform, including RAG, tool-calling, evals, guardrails, and monitoring for cost/latency/quality. Requires prior experience shipping LLM apps with strong RAG and production engineering skills.

## Job Description

## What you'll do
- Build user-facing LLM features: Design and implement retrieval-augmented generation and tool-calling flows using frameworks like LangChain or equivalent primitives.
- Deliver robust JSON and schema-bound outputs with validation, retries, and fallbacks.
- Add function calling to integrate with internal tools, search, routing, and data services.
- Own the service layer: Ship APIs and workers in Python or TypeScript with clear contracts, streaming, and backoff.
- Add caching, request shaping, prompt templates, and context packing to control latency and cost.
- Integrate with AWS Bedrock, OpenAI, Anthropic, or self-hosted endpoints as needed.
- Collaborate with infrastructure teammates to develop chunking, embeddings, and indexing capabilities for documents, time series, and multimedia.
- Choose and tune vector backends such as OpenSearch, pgvector, or Pinecone.
- Keep knowledge bases fresh with data syncs from S3, Aurora, DynamoDB, and external sources.
- Create offline evals and golden sets for prompts, retrievers, and tools.
- Stand up online metrics for task success, hallucination rate, retrieval precision/recall, p95 latency, and cost per request.
- Run A/B tests and prompt/version rollouts with guardrails and canaries.
- Implement content and policy checks, PII detection and redaction, access controls, and auditing.
- Design human-in-the-loop paths for sensitive actions.
- Handle aviation data with care and follow internal security standards.
- Add tracing, logs, and dashboards for model calls, token usage, errors, and saturation.
- Debug tricky failures across retrieval, prompts, tools, and providers.

## Requirements
- Shipped LLM apps: You’ve put LLM features in front of users and improved them with data.
- Strong builder: Comfortable writing production code, tests, and docs. You keep things simple and observable.
- RAG and tools depth: You understand embeddings, chunking, vector search tradeoffs, and function calling.
- Quality mindset: You design evals, define success metrics, and iterate based on evidence.
- Cost and latency aware: You track p95, hit SLAs, and reduce cost without hurting quality.
- Clear communicator: You explain tradeoffs and align partners across product, infra, and security.

## Nice-to-haves
- Experience with Bedrock, OpenSearch Serverless, pgvector, Pinecone, or Weaviate.
- Prompt versioning, guardrails, and provider routing in production.
- Multimodal work with time series or video.
- Familiarity with GPU inference, Triton, or TensorRT-LLM.
- Aviation or other safety-critical domain exposure.
- DevOps basics for CI/CD, IaC, and secure secrets handling.

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