Ml Ops Engineer

Summary

Join Inspiren, a company creating a platform to optimize senior living community operations, as a highly skilled ML Ops Engineer. You will lead ML Ops projects, collaborate with cross-functional teams, and innovate to improve machine learning pipelines. Responsibilities include overseeing model development and deployment, ensuring quality and compliance, and mentoring team members. The ideal candidate possesses a Bachelor's or Master's degree, 5+ years of ML Ops experience, and expertise in various technologies. The position offers a competitive salary ($170,000-200,000), equity, benefits (medical, dental, vision), and flexible PTO. The role is remote and US-based.

Requirements

• Educational Background: Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a related field
• Experience: 5+ years of hands-on experience in ML Ops, having successfully launched and managed multiple machine learning projects in production
• Technical Expertise: Programming Proficiency: Expertise in Python and familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn
• ML Pipeline Tools: Experience with tools like MLflow, Kubeflow, Airflow, or similar workflow orchestration tools
• Cloud Platforms: Proficiency in cloud platforms such as AWS, GCP, or Azure, particularly in AI/ML services and infrastructure management
• Containerization and Orchestration: Hands-on experience with Docker, Kubernetes, and CI/CD pipelines
• Data Engineering: Familiarity with data pipelines, ETL processes, and tools such as Apache Spark, Kafka, or Snowflake
• Model Monitoring and Optimization: Expertise in monitoring model performance and implementing automated retraining workflows
• Security Principles: Understanding of data security and privacy best practices in the context of machine learning
• Development Processes: Well-versed in Agile/Scrum methodologies and MLOps best practices
• Communication: Excellent verbal and written communication skills, with the ability to convey complex ideas clearly
• Adaptability: Comfortable working in a fast-paced, dynamic environment and adapting to changing priorities

Responsibilities

• Lead ML Ops Projects: Oversee the end-to-end development and deployment of machine learning models and infrastructure, from conceptualization to production and continuous improvement
• Collaborate Cross-Functionally: Work closely with data scientists, software engineers, product managers, DevOps teams, and other stakeholders to define and implement scalable ML pipelines and infrastructure aligned with product needs
• Innovate and Optimize: Stay current with industry trends and emerging technologies in machine learning operations. Introduce new methodologies, tools, and technologies to enhance performance and streamline workflows. Provide technical expertise in ML model deployment, monitoring, and optimization
• Embed Rigorous Design for Excellence (DfX) Mindset: Conduct infrastructure reviews and failure mode effect analysis (FMEA). Partner with cross-functional teams to drive rigorous DfX (design for scalability, reliability, performance, and cost-efficiency) methodologies across all phases of ML pipeline development
• Mentor Team Members: Provide technical guidance and mentorship, fostering a culture of excellence, innovation, and continuous learning
• Ensure Quality, Reliability, and Compliance: Establish and oversee best practices for model validation, monitoring, and performance tracking. Ensure deployed ML models meet regulatory standards, ethical AI principles, and industry best practices
• Problem-Solve: Troubleshoot complex ML pipeline issues and implement effective solutions in a timely manner. Act as Tier-2 engineering support for ML systems in production
• Strategic Planning: Contribute to the long-term ML roadmap, aligning development with the company’s product and platform roadmap

Preferred Qualifications

Start-up experience is a plus

Benefits

• The annual salary for this role is between $170,000-200,000 + equity + benefits (including medical, dental, and vision)
• Flexible PTO
• Location: Remote, US