Role & responsibilities :

- 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.

Preferred candidate profile :

- 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 Well Collaborate :

- This is a hands-on IC role - not people management. You will 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 have built and shipped, not the label.

Minimum Qualifications :

- Bachelors degree in CS/EE or equivalent practical experience.

- 3-5+ years in software/ML engineering with 1 year owning production AI/ML systems.