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Beacon Venture Capital
Beacon Venture Capital
1 – 10 Employees
Artificial IntelligenceFintech
Beacon Venture Capital is the wholly owned corporate venture capital arm of Kasikornbank PLC, a leading commercial bank in Thailand. Established in 2016, the fund invests strategically in technology startups from early through growth stages, with a primary focus on fintech. Its investment interests also include consumer lifestyle businesses and deep technology areas such as artificial intelligence and enterprise IT. Beacon Venture Capital launched with $30 million in capital and grew to $135 million by 2018.
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Staff Software Engineer, Artificial Intelligence and LLM

Lead development of LLM-powered aviation capabilities for Beacon AI’s safer-flight platform, covering RAG, tool calling, evaluation, safety, and production reliability.

USUnited StatesCACalifornia
$224,000 – $260,000 / year
HybridFull-timeLeadEnglish RequiredAI EngineerMachine Learning Engineer
AWSDynamoDBPythonTypeScript

Description

  • Build user-facing LLM product capabilities from design through production
  • Architect retrieval-augmented generation and tool-calling workflows
  • Produce reliable JSON and schema-constrained outputs with validation, retries, and fallback handling
  • Use function calling to connect internal tools, search, routing, and data services
  • Develop Python or TypeScript APIs and workers with explicit contracts, streaming, and backoff
  • Use caching, request shaping, prompt templates, and context packing to manage latency and cost
  • Connect applications to AWS Bedrock, OpenAI, Anthropic, or self-hosted model endpoints
  • Work with infrastructure engineers on chunking, embeddings, and indexing for documents, time series, and multimedia
  • Select and optimize vector databases including OpenSearch, pgvector, and Pinecone
  • Keep knowledge bases current by synchronizing data from S3, Aurora, DynamoDB, and external sources
  • Build offline evaluations and golden datasets for prompts, retrievers, and tools
  • Define online measures for task success, hallucination rate, retrieval precision and recall, p95 latency, and per-request cost
  • Conduct A/B tests and roll out prompt and version changes with guardrails and canary releases
  • Implement content and policy enforcement, PII detection and redaction, access controls, and audit trails
  • Create human-in-the-loop workflows for sensitive operations
  • Process aviation data in line with internal security standards
  • Provide tracing, logging, and dashboards for model calls, token usage, errors, and system saturation
  • Investigate failures across retrieval, prompts, tools, and model providers
  • Set technical direction across services and teams for complex, ambiguous problems
  • Work with ML, infrastructure, and product partners on user experience, outcomes, reliability, and safety-critical systems

Requirements

  • At least 8 years of experience, including ownership of systems or standards used by other engineers
  • Alternatively, a related master’s degree plus 4 years of relevant experience
  • Experience launching LLM features for end users and improving them through data
  • Strong production development, testing, and technical documentation skills
  • Knowledge of embeddings, chunking, vector-search tradeoffs, and function calling
  • Experience designing evaluations, establishing success metrics, and iterating from evidence
  • Ability to monitor p95 latency, satisfy SLAs, and lower costs while preserving quality
  • Ability to communicate tradeoffs and align product, infrastructure, and security stakeholders
  • Technical leadership experience setting direction or standards adopted by engineers or teams
  • Proficiency in Python or TypeScript
  • Experience with RAG, tool-calling workflows, schema-constrained JSON, validation, retries, fallbacks, APIs, workers, streaming, backoff, caching, prompt templates, context packing, and model providers
  • Experience using AWS Bedrock, OpenAI, Anthropic, or self-hosted endpoints
  • Familiarity with OpenSearch, pgvector, Pinecone, or Weaviate
  • Experience working with S3, Aurora, and DynamoDB data sources
  • Experience with offline evaluation, golden datasets, online metrics, A/B testing, prompt and version rollouts, guardrails, and canary releases
  • Experience implementing content and policy controls, PII detection and redaction, access management, and auditing
  • Experience with tracing, logging, dashboards, model-call troubleshooting, and production operations
  • U.S. Person status is required; visa sponsorship and transfers are not available
  • All work must be performed within the United States
  • Ability to work in the San Francisco Bay Area or San Carlos, California, at least 3 days per week onsite

Benefits

  • Equity is offered
  • The company covers 100% of employee medical premiums
  • The company covers 25% of dependent medical premiums
  • Three weeks of paid time off
  • At least 13 paid company holidays
  • 401(k) plan offered
  • Remote work is available on the remaining hybrid-schedule days

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