Lead MLOps Engineer; Remote EST

Job Description A remote-first technology company transforming how organizations operationalize artificial intelligence is seeking a Lead MLOps Engineer to join its growing engineering team. Candidates must be based within the Eastern Time Zone to support collaboration across product, engineering, and leadership stakeholders.

This is a senior-level opportunity for an engineer who has built and operated machine learning platforms that support real-world production workloads. Rather than focusing solely on model development, this role centers on creating the infrastructure, deployment frameworks, governance standards, and operational processes that allow AI systems to run reliably at scale.

You will be responsible for bridging the gap between software engineering and machine learning by creating the systems that enable models, AI services, and intelligent applications to move efficiently from development to production. Success in this role requires deep cloud expertise, platform engineering experience, and a passion for building highly available systems that support business-critical AI initiatives.

• 10+ years of experience in software engineering, infrastructure engineering, or platform development
• Proven experience leading MLOps initiatives in enterprise production environments
• Strong background designing highly scalable distributed applications and services
• Expertise deploying, managing, and optimizing machine learning models in production
• Experience supporting Generative AI and Large Language Model (LLM) applications
• Deep understanding of CI/CD pipelines for AI and machine learning workloads
• Hands‑on experience with model registries, versioning strategies, and deployment automation
• Advanced proficiency developing production software with Python and Type Script
• Strong experience designing and consuming RESTful APIs
• Expertise building solutions within AWS cloud environments
• Experience working with Docker and container orchestration technologies
• Knowledge of Infrastructure as Code methodologies including AWS CDK and Cloud Formation
• Experience implementing monitoring, logging, alerting, and reliability practices
• Understanding of security best practices for cloud‑native and multi‑tenant platforms
• Ability to lead technical discussions, mentor engineers, and drive engineering standards

• Experience supporting Retrieval‑Augmented Generation (RAG) implementations
• Knowledge of vector databases, embeddings, and semantic search architectures
• Familiarity with AI agent frameworks and model orchestration technologies
• Experience creating automated model evaluation and validation pipelines
• Understanding of scalable GPU infrastructure and inference optimization
• Exposure to scientific computing, healthcare, life sciences, or regulated industries
• Experience implementing AI governance, auditing, and compliance controls
• Familiarity with performance tuning techniques for model serving environments
• Experience improving development workflows through automation and platform tooling

• Own the operational strategy for machine learning infrastructure and deployment platforms
• Build frameworks that accelerate the delivery of AI solutions from experimentation to production
• Create repeatable deployment processes that improve reliability and reduce operational risk
• Develop platform capabilities that support model serving, monitoring, and governance
• Implement automation that simplifies infrastructure provisioning and software releases
• Establish observability practices across AI services, APIs, and backend systems
• Partner with machine learning engineers to improve model performance and operational readiness
• Drive platform scalability initiatives that…