Software Engineer, Cloud Infrastructure

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

• 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.

What Will Make You Successful

• End-to-End Ownership: Drives work from design through production, including on-call and continuous improvement.
• LLM Systems Experience: Shipped or operated LLM-powered applications in production. Familiar with RAG design, prompt versioning, and chain orchestration using LangChain or similar.
• AWS Depth: Strong with core AWS services such as VPC, IAM, KMS, CloudWatch, S3, ECS/EKS, Lambda, Step Functions, Bedrock, and SageMaker.
• Data Engineering Skills: Comfortable building ingestion and transformation pipelines in Python. Familiar with Glue, Athena, and event-driven patterns using EventBridge and SQS.
• Security Mindset: Applies least privilege, secrets management, network isolation, and compliance practices appropriate to sensitive data.
• Evaluation and Metrics: 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.

Bonus Points

• 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.