Machine Learning Network Software Engineer

Role: Machine Learning Network Software EngineerLocation: Raynham, MA - Contract W2 (2 days onsite)Duration: 2 years About the Role:We are seeking a high-level Machine Learning Network Software Engineer to strategically design and stand up a scalable ML network infrastructure supporting translational research and enterprise operations. This is a high-visibility engagement at the intersection of machine learning, network engineering, and enterprise architecture, offering the opportunity to build a modern ML platform from the ground up within a large, complex organization in the healthcare and life sciences space.The consultant will assess an existing enterprise ML ecosystem, identify dependencies, and architect a scalable, secure, and compliant standalone ML platform. This role combines hands-on technical leadership with strategic planning, offering strong visibility across data science, infrastructure, security, and enterprise architecture teams on a global scale.Key Responsibilities:Assess and document the current-state ML platform, network architecture, data pipelines, and operational processes.Develop a migration strategy and future-state architecture for a standalone ML environment.Design and implement ML infrastructure, MLOps pipelines, networking, and cloud-based platforms.Lead separation and migration activities, including dependency mapping, risk mitigation, and cutover planning.Establish governance, security, monitoring, and compliance frameworks aligned with healthcare and medical device regulations.Collaborate with Data Science, Infrastructure, Security, Enterprise Architecture, and business stakeholders globally.Required Qualifications:10+ years of software engineering, infrastructure engineering, or enterprise architecture experience.5+ years designing and supporting enterprise ML platforms and MLOps environments.Proven experience leading technology carve-outs, divestitures, mergers, or large-scale platform migrations.Expertise with cloud platforms (AWS, Azure, and/or GCP), Kubernetes, Docker, Terraform, CI/CD, and Infrastructure as Code.Strong programming skills in Python and experience with distributed data and ML systems.Excellent stakeholder management and executive communication skills.Preferred Qualifications:Experience in healthcare, life sciences, pharmaceuticals, or medical devices.Knowledge of ML platforms such as Databricks, Azure ML, SageMaker, Kubeflow, or MLflow.Familiarity with FDA, HIPAA, GDPR, and other regulated-environment requirements.Deliverables:Current-state assessment of the existing ML ecosystem.Future-state architecture and transition roadmap.Standalone ML network and platform environment.Successful migration of critical ML workloads and operational processes.Security, governance, and support framework enabling long-term scalability and independence.Why This Role:Greenfield opportunity to architect a modern ML platform from the ground up.High-visibility engagement with executive and cross-functional exposure.Long-term engagement potential (6+ months, likely up to 2 years).Hybrid flexibility with a predictable, structured onsite schedule.