Lead MLOps Engineer; Remote in MA)

Job Description A rapidly growing AI and data platform organization is seeking a Lead MLOps Engineer / Software Platform Engineer to help build and scale the infrastructure powering next‑generation machine learning and generative AI applications. This fully remote opportunity is open to professionals residing in Massachusetts who want to play a key role in designing cloud-native AI systems used in highly regulated environments.

In this position, you will lead the architecture, deployment, and operational strategy for production‑grade AI services, machine learning platforms, and large language model (LLM) solutions. You will work closely with AI engineers, data teams, and platform engineers to create scalable, secure, and resilient infrastructure that supports both customer-facing and internal AI workloads.

This role is ideal for someone who has successfully built and operated AI/ML platforms serving external users, understands the challenges of distributed systems at scale, and enjoys driving technical direction across engineering organizations.

• 10+ years of professional software engineering and infrastructure engineering experience
• Proven expertise designing and scaling cloud-native distributed systems in production environments
• Strong background leading technical architecture decisions for large‑scale platforms
• Extensive experience building and operating AI/ML infrastructure as a customer‑facing product
• Hands‑on experience deploying and supporting LLM‑based applications in production
• Deep knowledge of Retrieval‑Augmented Generation (RAG), prompts, embeddings, and model lifecycle management
• Advanced programming skills in Python and Type Script
• Experience with model deployment, versioning, and model‑serving platforms
• Strong understanding of API architecture, REST services, and OpenAPI specifications
• AWS expertise and experience working with containerized environments such as Docker
• Experience with Infrastructure as Code tools, including Cloud Formation or CDK
• Knowledge of observability, monitoring, alerting, tracing, and SLI/SLO/SLA practices
• Experience implementing security controls for multi‑tenant environments, including PII and PHI protection
• Ability to mentor engineers and influence architecture across multiple teams

• Familiarity with modern LLM orchestration frameworks
• Experience supporting agent‑based AI systems in production
• Knowledge of model serving optimization, inference tuning, and cost management
• Experience with multimodal AI solutions involving image or scientific datasets
• Background in model optimization techniques such as quantization or distillation
• Experience working in regulated industries with compliance requirements
• Exposure to life sciences, healthcare, laboratory systems, or scientific data platforms
• Understanding of AI governance, evaluation frameworks, and quality monitoring

• Lead the design and evolution of the organization’s AI and machine learning platform
• Define architecture standards for model serving, agent execution, and scalable AI workloads
• Manage model lifecycle processes including deployment, versioning, promotion, rollback, and governance
• Build infrastructure supporting both real‑time and batch AI processing
• Design secure, multi‑tenant AI environments with strong data isolation controls
• Develop evaluation frameworks, testing pipelines, and release gates for AI systems
• Establish observability, monitoring, and operational excellence standards
• Drive platform reliability, scalability, and cost‑efficiency initiatives
• Partner with engineering, security, and data teams to deliver enterprise‑grade AI solutions
• Lead design reviews and provide technical mentorship to engineering teams
• Evaluate emerging AI…