AWS Cloud & Data Platform Engineer

AWS Cloud & Data Platform Engineer

6-Month Contract (Potential Path to Full-Time)

               Company Overview Enablence USA Components, Inc. is a leading provider of innovative integrated optical products serving the communications, aerospace, and bio-chemical sensing industries. Our globally marketed products have been integral to numerous fiber-optic networks worldwide, and we are at the forefront of developing photonic integrated circuits (PICs) based on silicon platforms, including high-speed optical sub-assemblies for metro-area and data center interconnections. Engagement Overview We're engaging an AWS Cloud & Data Platform Engineer for a 6-month, hands-on build contract: automating our infrastructure provisioning, standing up a new cloud-native data platform, and contributing to a production ML delivery pipeline. Strong performers will be considered for a full-time role on the team afterward — we're using this contract as a genuine two-way trial, not just a staffing gap-filler. What We're Looking For Beyond the technical bar, we're evaluating fit for a permanent seat on the team. That means someone who:

• Communicates clearly and proactively about progress, tradeoffs, and blockers — without needing to be chased

• Takes ownership of ambiguous problems and makes calls independently, checking in at the right moments rather than every step

• Works well embedded with an in-house team, not at arm's length like a typical short-term contractor

• Is direct and honest about what's working, what isn't, and what should change Key Responsibilities Infrastructure Automation (Core)

• Provision multiple environments (dev/test/prod) via Infrastructure as Code (Terraform strongly preferred)

• Design auto-scaling for services with variable, at-times-predictable traffic, combining scheduled and reactive scaling

• Implement health-check-based self-healing for automatic recovery of failed service instances Data Platform (Core)

• Design and build a new, AWS-native data platform (S3, Glue, Athena) from the ground up

• Design query/access interfaces as clean, well-documented APIs suitable for internal use and downstream automation ML Pipeline & Delivery (Contributing / Growth Area)

• Contribute to a SageMaker Pipelines–based ML framework supporting multiple model types

• Help build retrain triggers (manual + automated) and model registry/deployment automation

• Support serving an existing computer vision model via REST API to a consuming application

• Deep prior ML pipeline expertise is not required — genuine interest and aptitude to grow into this, backed by good engineering fundamentals, is enough Cross-Cutting

• Use AI coding agents to accelerate infrastructure, pipeline, and API development, paired with rigorous code review and testing discipline

• Document architecture and provide knowledge transfer to the internal team Required Qualifications (Must-Have)

• 5+ years AWS engineering/architecture experience

• Hands-on Infrastructure as Code experience across multiple environments (Terraform strongly preferred)

• Experience designing auto-scaling and self-healing for production services

• Experience building data platforms on AWS-native services (S3, Glue, Athena) from scratch

• Experience designing REST/GraphQL APIs, including for downstream/programmatic consumers

• Demonstrated, disciplined use of AI coding agents on real projects — with strong code review and testing practices

• Strong communication skills and comfort collaborating closely with an in-house team

• Comfortable owning ambiguous problems as an independent contractor, with an eye toward a longer-term fit Preferred / Nice to Have

• Experience with SageMaker Pipelines (or equivalent) — training, registry, deployment

• Exposure to AutoML tooling (e.g., SageMaker Autopilot)

• Computer vision model experience

• Python/Django experience (for integration work)

• AWS certifications (Solutions Architect Associate/Professional, Machine Learning Specialty)

• Experience with model drift/performance monitoring Engagement Details

• Duration: 6 months, phased delivery (infrastructure → data platform → ML pipeline); strong performers considered for a full-time role afterward

• Engagement type: Contract Work Location: Remote Application Process: Send resume to [email protected]