10+ years in Software Engineering, Platform Engineering, DevOps, SRE, or related fields
2+ years of hands-on MLOps experience in production
Experience with training, deploying, and operating machine learning models at scale
Strong understanding of ML lifecycle management
Expertise in building infrastructure for model training and deployment
Proficient with cloud platforms, preferably AWS
Strong background in Kubernetes and containerized applications
Experience with CI/CD automation and infrastructure-as-code practices
Proficient in Python and modern software engineering practices
Strong communication and technical leadership skills
Responsibilities
Lead the design and development of scalable platform infrastructure for ML workloads
Build and optimize model training pipelines and deployment frameworks
Develop processes and tooling for reliable model delivery
Create observability solutions to monitor model performance and operational health
Partner with Data Science and Engineering teams to enhance ML development efficiency
Establish best practices for MLOps and platform reliability
Drive improvements in cloud infrastructure and deployment automation
Mentor engineers and provide technical leadership
Troubleshoot complex production issues involving AI and ML services
Ensure high availability and security of model-serving environments
Benefits
Medical Insurance
Dental Benefits
Vision Benefits
Paid Time Off (PTO)
401(k)
Full Job Description
Job Description A technology-driven organization in Cambridge, MA is hiring a Lead Platform Engineer to help build and scale the infrastructure powering next-generation AI and machine learning systems. This is a highly visible leadership opportunity for an experienced engineer who has successfully taken machine learning initiatives from research through production deployment at scale. This role is ideal for someone who combines deep platform engineering expertise with hands-on MLOps experience. You will partner with data scientists, machine learning engineers, and infrastructure teams to develop robust systems for model training, deployment, monitoring, and lifecycle management. The successful candidate will play a key role in establishing best practices for production AI workloads while ensuring reliability, observability, and operational excellence. Required Skills & Experience
10+ years of experience in Software Engineering, Platform Engineering, DevOps, SRE, or related disciplines
2+ years of hands-on MLOps experience in production environments
Experience training, deploying, and operating machine learning models at scale
Strong understanding of ML lifecycle management from experimentation through production
Experience building infrastructure for model training pipelines and model deployment workflows
Expertise with cloud platforms, preferably AWS
Experience implementing observability, monitoring, and alerting solutions for machine learning systems
Strong background with Kubernetes and containerized applications
Experience with CI/CD automation and infrastructure-as-code practices
Proficiency with Python and modern software engineering best practices
Strong communication and technical leadership skills
Desired Skills & Experience
Experience supporting GenAI, LLM, or Agentic AI applications
Familiarity with ML orchestration frameworks such as Kubeflow, MLflow, SageMaker, Airflow, or similar technologies
Experience with distributed training environments and GPU infrastructure
Background in platform architecture and technical leadership
Experience designing secure and scalable cloud-native systems
Exposure to data engineering and large-scale data platforms
What You Will Be Doing Daily Responsibilities
Lead the design and development of scalable platform infrastructure supporting machine learning workloads
Build and optimize model training pipelines and deployment frameworks
Develop processes and tooling for reliable model delivery into production environments
Create observability solutions to monitor model performance, drift, latency, availability, and operational health
Partner with Data Science and Engineering teams to improve the efficiency of the ML development lifecycle
Establish best practices for MLOps, automation, and platform reliability
Drive improvements in cloud infrastructure, Kubernetes operations, and deployment automation
Mentor engineers and provide technical leadership across platform initiatives
Troubleshoot complex production issues involving AI and ML services
Ensure high availability, scalability, and security of model-serving environments
