MLOps Engineer II

EagleView, the leader in aerial imagery, is hiring a MLOps in AI & Machine Learning.

As an MLOps Engineer II, you will play a key role in designing, building, and operating scalable and reliable machine learning platform and production inferencing. You will work closely with Data Scientists and Platform teams to operationalize end-to-end ML workflows on AWS, ensuring models move seamlessly from experimentation to production and monitoring.

In this role, you are expected to operate with a high degree of ownership, contribute to architectural decisions, and mentor junior engineers and interns. You will also contribute to advanced initiatives such as Agentic AI systems and MCP servers, helping the team adopt emerging AI infrastructure patterns while maintaining strong MLOps fundamentals.

• Design, build, deploy, and maintain production-grade ML pipelines and workflows using AWS and Python, with a focus on reliability, scalability, and observability.
• Own and enhance the MLOps platform that automates the full ML model lifecycle—from data annotation and training to inference, monitoring, and feedback loops.
• Collaborate closely with Data Scientists to productionize models, including packaging, versioning, deployment strategies, and performance optimization.
• Contribute to Agentic AI initiatives, including evaluation and deployment of MCP servers and related infrastructure components.
• Implement monitoring, logging, alerting, and CI/CD best practices for ML systems to ensure production stability and rapid issue resolution.
• Troubleshoot complex pipeline, infrastructure, and inference issues, performing root cause analysis and driving long-term fixes.
• Stay current with evolving MLOps practices, cloud-native ML tooling, and emerging AI infrastructure trends, and proactively introduce improvements.
• Participate in design reviews, technical discussions, and planning meetings; clearly communicate progress, risks, and trade-offs to stakeholders.
• Mentor interns and junior engineers by providing technical guidance, code reviews, and best practices.

• 3–6 years of hands-on experience building and operating ML or data platforms, with a strong focus on MLOps or ML infrastructure.
• Strong practical experience with AWS services such as Sagemaker, S3, EC2, Batch, Lambda, IAM, and monitoring tools.
• Proficiency in Python for building ML pipelines, automation, and infrastructure tooling.
• Solid understanding of the ML lifecycle, including training, evaluation, deployment, inference, and model monitoring.
• Experience with containerization (Docker) and familiarity with orchestration frameworks (e.g., Kubernetes or managed equivalents).
• Strong problem-solving skills and the ability to independently drive tasks in a fast-paced, evolving environment.
• Effective communication skills and experience collaborating across Data Science and Engineering teams.

Preferred Experience

• Experience designing or operating end-to-end MLOps platforms supporting multiple models, teams, or use cases.
• Familiarity with CI/CD systems and Git-based workflows.
• Hands-on experience with ML inference systems (real-time or batch), including performance tuning and cost optimization.
• Exposure to or active work in Agentic AI, GenAI infrastructure, or MCP servers.
• Demonstrated ability to mentor junior engineers and raise overall team engineering quality.
• Strong aptitude for evaluating and adopting new technologies as AI and MLOps ecosystems evolve.

This job description is not an exclusive or exhaustive list of all job functions that a workforce member in this position may be asked to perform. Duties and responsibilities can be changed, expanded, reduced, or delegated by management to meet the business needs of the company.