ML Engineer

BIRDSVUE LLC

Dunstable, MA Job Details Full-time $50 - $55 an hour 1 day ago Qualifications Performance monitoring Cloud identity and access management (IAM) Azure IaaS Infrastructure as Code (IaC) GitHub Actions IT monitoring tools Terraform Cloud networking Developing automated testing protocols Monitoring system implementation Cloud automation Technical troubleshooting support Cloud solution engineering DevOps automation Cloud monitoring Debugging Production troubleshooting Identity & access management Full Job Description ML Engineer 1. Role description Build and improve Client's AI Platform. The primary goal is to design and implement the core

REST API

that powers the platform. Collaborate with other IT teams (e.g., security, cloud etc.) to deliver a reliable, secure and easy-to-use AI platform for teams to use. 2. Skills Core skills include Python and asynchronous

REST API

development using FastAPI, relational database design, and infrastructure automation using Terraform. Strong Microsoft Azure experience is required, including identity and access management, secrets management and private networking, alongside Git, GitHub Actions, CI/CD pipelines, and automated testing. Strong troubleshooting, debugging, and error-handling skills, with experience implementing effective logging, monitoring, and observability practices, are also required. Familiarity with Azure Machine Learning, Microsoft Foundry, and MLOps practices is a plus. 3. Desired background Demonstrated experience delivering production backend services, cloud platforms, or self-service infrastructure tooling, ideally supporting AI and machine learning teams. Strong hands-on experience with Microsoft Azure and Infrastructure as Code using Terraform is required; experience in energy, utilities, or other regulated enterprise environments is a plus. Able to own end-to-end feature delivery across APIs, databases, infrastructure, and deployment pipelines, while maintaining and improving an existing codebase. Experienced in collaborating with security, cloud, and networking teams to meet enterprise requirements. Produces maintainable, well-tested code, clear technical documentation and handovers, and can effectively communicate architecture decisions and technical trade-offs to both technical and non-technical stakeholders. 4.

Timeline Target start:

November 2, 2026, or the earliest practical date after selection, onboarding, and access approval.

Expected duration:

Six months initially; any extension subject to funding and delivery needs. 5. Headcount One full-time ML Engineer, approximately 40 hours per week, subject to contract.