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 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.

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 Experience

• GenAI 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 Collaborate

• This 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 Qualifications

• Bachelor’s degree in CS/EE or equivalent practical experience.
• 3–5+ years in software/ML engineering with ≥1 year owning production AI/ML systems.