AI Product Engineer

AI Product Engineer:\n\nWe’re looking for a hands‑on Individual Contributor who excels at taking real business problems and turning them into production‑ready GenAI solutions. 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.\n\nWhat You’ll Do\nOwn problems end‑to‑end : Translate business goals into technical plans; design pragmatic solutions; deliver production systems with measurable impact.\nBuild production back ends : Design and implement APIs and microservices (REST/gRPC) for GenAI workloads; containerize and orchestrate services (Docker/Kubernetes/ECS/EKS).\nShip 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.\nWork across modalities : Deliver features for text (LLMs/RAG) and images (VLMs/CV) including retrieval, embeddings, fine‑tuning/adapters, and evaluation pipelines.\nMake it observable : Instrument logging, metrics, and traces (OpenTelemetry/CloudWatch/Datadog/etc.); build dashboards, SLOs/SLIs, and alerts; own performance, reliability, and cost.\nValidate and govern : Implement offline/online evaluations, A/B tests, guardrails/red‑teaming, data and model quality checks, and safety/compliance gates.\nAutomate the path to prod : Establish CI/CD (GitHub Actions/CodePipeline), infrastructure as code (Terraform/CloudFormation), automated tests, and rollouts (canary/blue‑green).\nCollaborate without handoffs : Partner with product, domain experts, and downstream teams; document architecture; support launches; close the loop with data‑driven iteration.\n\nWhat You’ve Shipped (Signals We’ll Look For)\nAt least one year owning a production GenAI or ML system (not a side project), plus 3–5+ years total professional experience building back‑end or ML‑powered products.\nServices you built that are running in production with users/traffic, clear SLIs/SLOs, and release/incident history.\nEvidence of quality: eval frameworks, regression tests, canary strategies, monitoring dashboards, cost/perf optimizations you introduced.\n\nRequired Experience\nGenAI foundation : LLMs/VLMs, embeddings, RAG, prompt orchestration, adapters/fine‑tuning, tokenization, latency/cost trade‑offs, content safety/guardrails.\nBack‑end & systems : Strong design of microservices, APIs, event‑driven patterns; data modeling across SQL/NoSQL; familiarity with vector databases .\nAWS & cloud infra : IAM/KMS/secrets, networking, containers/orchestration, CI/CD, IaC; operating services in AWS with cost/performance ownership.\nObservability & reliability : Logging, metrics, traces; performance profiling; incident response; chaos and load testing; availability and scaling strategies.\nLanguages & tooling : Proficient in Python (plus one of TypeScript/Go/Java); PyTorch/TensorFlow; Docker/Kubernetes; git; testing frameworks.\n\nHow We’ll Collaborate\nThis is a hands‑on IC role — not people management. You’ll partner closely with product and customers and will be expected to roll up your sleeves daily.\nTitle is flexible (e.g., AI Systems Engineer, AI Product Engineer, Senior Software Engineer — AI, ML Engineer (Production)).