AI Product Engineer

AI Product Engineer:We’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.What You’ll DoOwn 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.What You’ve Shipped (Signals We’ll Look For)At 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.Services you built that are running in production with users/traffic, clear SLIs/SLOs, and release/incident history.Evidence of quality: eval frameworks, regression tests, canary strategies, monitoring dashboards, cost/perf optimizations you introduced.Required ExperienceGenAI foundation: LLMs/VLMs, embeddings, RAG, prompt orchestration, adapters/fine‑tuning, tokenization, latency/cost trade‑offs, content safety/guardrails.Back‑end & systems: Strong design of microservices, APIs, event‑driven patterns; data modeling across SQL/NoSQL; familiarity with vector databases.AWS & cloud infra: IAM/KMS/secrets, networking, containers/orchestration, CI/CD, IaC; operating services in AWS with cost/performance ownership.Observability & reliability: Logging, metrics, traces; performance profiling; incident response; chaos and load testing; availability and scaling strategies.Languages & tooling: Proficient in Python (plus one of TypeScript/Go/Java); PyTorch/TensorFlow; Docker/Kubernetes; git; testing frameworks.How We’ll CollaborateThis 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.Title is flexible (e.g., AI Systems Engineer, AI Product Engineer, Senior Software Engineer — AI, ML Engineer (Production)). We care about what you’ve built and shipped, not the label.Minimum QualificationsBachelor’s degree in CS/EE or equivalent practical experience.3–5+ years in software/ML engineering with ≥1 year owning production AI/ML systems.

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