Senior Software Engineer, Cloud Infrastructure
Senior engineer who designs, builds, and operates Beacon AI's AWS cloud foundation and LLM platform — IaC-managed infrastructure, CI/CD, secure networking, RAG/vector-search data systems, model endpoints (Bedrock/SageMaker), and observability for AI-powered aviation products. Hybrid role based in San Carlos, CA.
- Compensation
- $173K–$225K / yr
- Location
- San Carlos - Hybrid
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Full time
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- End-to-end ownership: drives work from design through production, including on-call and continuous improvement
- Shipped or operated LLM-powered applications in production; familiar with RAG design, prompt versioning, and chain orchestration using LangChain or similar
- Strong with core AWS services: VPC, IAM, KMS, CloudWatch, S3, ECS/EKS, Lambda, Step Functions, Bedrock, SageMaker
- Comfortable building ingestion and transformation pipelines in Python; familiar with Glue, Athena, EventBridge, and SQS
- Security mindset: least privilege, secrets management, network isolation, and compliance practices for sensitive data
- Uses quantitative evals, A/B testing, and live metrics to guide improvements
- Clear communication: explains tradeoffs and aligns partners across product, security, and application engineering
- Must be a U.S. Person (citizen, Green Card holder, lawful permanent resident, or asylee/refugee status) due to export control; all work in the United States
Nice to have
- 4+ years working with serverless or container platforms on AWS
- Experience with vector databases, OpenSearch, or pgvector at scale
- Hands-on with Bedrock Guardrails, Knowledge Bases, or custom policy engines
- Familiarity with GPU workloads, Triton Inference Server, or TensorRT-LLM
- Experience with big data tools for large-scale processing and search
- Background in aviation data or other safety-critical domains
- DevOps or DevSecOps experience automating CI/CD for ML and app services
Full description
About Beacon AI
We’re a fast-moving team of aviators, engineers, and operators building an AI platform to make flying safer, more efficient, and more capable. Backed by top investors, we’ve secured a dozen Department of Defense contracts and partnered with major airlines to deliver mission-critical systems. We operate without silos or heavy processes. Small, focused teams own what they build, ship quickly, and learn fast, pushing the boundaries of how humans and AI work together in aviation.
Role Overview
We are seeking skilled Senior Software Engineers, Cloud Infrastructure to lead the buildout of our AWS foundation and our LLM platform. You will design, implement, and operate services that are scalable, reliable, and secure.
The broad scope means focus areas in LLM/ML Infra and IoT infra are strong bonus points. For ML Infra, build the stack that powers retrieval-augmented generation and application workflows built with frameworks like LangChain. Experience with IoT AWS services is a plus.
You will work closely with other engineers and product management. The ideal candidate is hands-on, comfortable with ambiguity, and excited to build from first principles.
Key Responsibilities
- Cloud Infrastructure Setup and Maintenance
- Design, provision, and maintain AWS infrastructure using IaC tools such as AWS CDK or Terraform.
- Build CI/CD and testing for apps, infra, and ML pipelines using GitHub Actions, CodeBuild, and CodePipeline.
- Operate secure networking with VPCs, PrivateLink, and VPC endpoints. Manage IAM, KMS, Secrets Manager, and audit logging.
- LLM Platform and Runtime
- Stand up and operate model endpoints using AWS Bedrock and/or SageMaker; evaluate when to use ECS/EKS, Lambda, or Batch for inference jobs.
- Build and maintain application services that call LLMs through clean APIs, with streaming, batching, and backoff strategies.
- Implement prompt and tool execution flows with LangChain or similar, including agent tools and function calling.
- RAG Data Systems and Vector Search
- Design chunking and embedding pipelines for documents, time series, and multimedia. Orchestrate with Step Functions or Airflow.
- Operate vector search using OpenSearch Serverless, Aurora PostgreSQL with pgvector, or Pinecone. Tune recall, latency, and cost.
- Build and maintain knowledge bases and data syncs from S3, Aurora, DynamoDB, and external sources.
- Evaluation, Observability, and Cost Governance