AI Systems Engineer

About the job

If you have a strong track record of designing, building, and operating GenAI systems (across both text and images) in production environments, we’d love to talk. What You’ll Do - Own problems end‑to‑end: Translate business goals into technical plans; design pragmatic solutions; deliver production systems with measurable impact. • Build production back ends: Design and implement APIs and microservices (REST/gRPC) for GenAI workloads; containerize and orchestrate services (Docker/Kubernetes/ECS/EKS). • Ship on AWS: Leverage AWS (e.g., Lambda, ECS/EKS, S3, DynamoDB/RDS, API Gateway, SQS/SNS, CloudWatch) plus AI services (e.g., Bedrock, SageMaker) to train, host, and integrate models. • Work across modalities: Deliver features for text (LLMs/RAG) and images (VLMs/CV) including retrieval, embeddings, fine‑tuning/adapters, and evaluation pipelines. • Make it observable: Instrument logging, metrics, and traces (OpenTelemetry/CloudWatch/Datadog/etc.); build dashboards, SLOs/SLIs, and alerts; own performance, reliability, and cost. • Validate and govern: Implement offline/online evaluations, A/B tests, guardrails/red‑teaming, data and model quality checks, and safety/compliance gates. • Automate the path to prod: Establish CI/CD (GitHub Actions/CodePipeline), infrastructure as code (Terraform/CloudFormation), automated tests, and rollouts (canary/blue‑green). • Collaborate without handoffs: Partner with product, domain experts, and downstream teams; document architecture; support launches; close the loop with data‑driven iteration.