Senior Software Engineer, Cloud Infrastructure

Beacon AI, San Carlos - Hybrid

The role

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.

All jobs

Senior Software Engineer, Cloud Infrastructure

Beacon AI, San Carlos - Hybrid

Posted pay

$173K–$225K

a year

Compared with similar jobs

This base pay is below the median for senior infrastructure, platform and SRE jobs in the Bay Area.

Median: $214K a year
Other jobs in the group: 626

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location: San Carlos - Hybrid
Work type: Not given
Level: Senior
Skills in the job text: AWS
Terraform
PostgreSQL
Airflow
DynamoDB
Elasticsearch
LangChain
Redis
Posted: Oct 2, 2026
Last check: 8 h ago
Job type: Infra, platform & SRE, Inference & ML systems, ML / AI engineering

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Beacon AI

Open tech jobs: 15
With posted pay: 100%
Median posted base: $173K–$225K

About Beacon AI: funding, hiring and pay

Beacon AI

About Beacon AI: funding, hiring and pay

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The role

What you will do

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
Create offline and online eval harnesses for prompts, retrievers, and chains. Track quality, latency, and regression risk.
Instrument model and app telemetry with CloudWatch and OpenTelemetry. Build token usage and cost dashboards with budgets and alerts.
Add guardrails, rate limits, fallbacks, and provider routing for resilience.
Safety, Privacy, and Compliance
Implement PII detection and redaction, access controls, content filters, and human-in-the-loop review where needed.
Use Bedrock Guardrails or policy services to enforce safety standards. Maintain audit trails for regulated environments.
Data Pipeline Construction
Build ingestion and processing pipelines for structured, unstructured, and multimedia data. Ensure integrity, lineage, and cataloging with Glue and Lake Formation.
Optimize bulk data movement and storage in S3, Glacier, and tiered storage. Use Athena for ad-hoc analysis.
IoT Deployment Management
Manage infrastructure that deploys to and communicates with edge devices. Support secure messaging, identity, and over-the-air updates.
Analytics and Application Support
Partner with product and application teams to integrate retrieval services, embeddings, and LLM chains into user-facing features.
Provide expert troubleshooting for cloud and ML services with an emphasis on uptime and performance.
Performance Optimization
Tune retrieval quality, context window use, and caching with Redis or Bedrock Knowledge Bases.
Optimize inference with model selection, quantization where applicable, GPU/CPU instance choices, and autoscaling strategies.

Senior Software Engineer, Cloud Infrastructure

The role

Senior Software Engineer, Cloud Infrastructure

The role

What you will do

Nice to have

What will make you successful

Pay and benefits

About Beacon AI

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