Okay, I've searched for Machine Learning Engineer roles in Washington, DC that are fully remote in the technology industry, requiring at least 3 years of experience and a Bachelor's degree in Computer Science. I have found 73 positions for you.

Sure. Here's the analysis:

Job Analysis:

This role of Machine Learning Engineer, AI Platform at Splunk is fundamentally about enabling and scaling AI-powered services that underpin a broad range of AI products. The engineer is expected to blend software engineering excellence with cloud infrastructure expertise and machine learning deployment know-how. Beyond coding, this extends to architecting reliable, scalable, and maintainable AI systems on the cloud using Infrastructure as Code, containerization, and automated CI/CD pipelines. The job demands collaboration with data scientists and ML engineers to optimize model serving, while also ensuring system observability, troubleshooting, and performance tuning. The requirement for deep experience with cloud platforms, container orchestration, and monitoring tools illustrates the complexity and scale at which these AI services operate. Success in this role means building resilient, efficient, and secure AI deployment pipelines that accelerate innovation and time-to-market for AI features. Autonomy and problem-solving skills are critical since the engineer will need to architect solutions under ambiguity, identify infrastructure bottlenecks, and continually improve deployment workflows. Moreover, soft skills such as clear communication and a proactive mindset are essential to harmonize cross-functional efforts and document sophisticated systems, which helps in sustaining long-term operational excellence.

Company Analysis:

Splunk, operating as part of Cisco, occupies a leading position in the AI and data analytics space with a strong customer focus on making machine data accessible and valuable through AI. The company culture appears innovation-driven yet collaborative and inclusive, emphasizing customer success and internal team cohesion. This role’s fully remote option complements a modern, flexible work culture that values adaptability and self-motivation. Positioned at the intersection of AI and cloud-native technologies, Splunk’s engineering teams are expected to push technological boundaries while maintaining high reliability—traits aligned with a dynamic, growth-focused enterprise within a large, stable ecosystem. The AI Platform team's work is strategically crucial: it directly supports Splunk’s expanding AI-driven product portfolio, making this hire a key enabler of scalable AI solutions. Being a part of this team offers visibility into both technical leadership and cross-team collaboration with scientists and engineers, suggesting that candidates who are proactive, communicative, and comfortable interfacing with varied stakeholders will thrive. In terms of long-term potential, the company’s commitment to diversity, total rewards, and technology innovation signals a place where continuous learning and career growth are possible, especially for engineers focused on MLOps and AI system scalability within a major enterprise player.

Absolutely. Here are some mock interview questions that could come up:

• Tell me about a time when you had to build or optimize a CI/CD pipeline for machine learning models. What challenges did you face and how did you overcome them?
• Describe a situation where you identified a bottleneck in a machine learning deployment system. How did you diagnose and resolve the issue?
• Can you share an experience where you worked closely with data scientists or ML engineers to deploy models into production? How did you manage communication and technical integration?
• Tell me about a project where you used Infrastructure as Code to manage cloud resources. How did this approach benefit the project?
• How would you approach designing an AI service that must scale dynamically based on demand using Kubernetes and Terraform?
• What strategies would you employ to ensure the security of ML models and data when deploying AI services on cloud platforms?
• Explain how you would monitor the performance and reliability of a machine learning model deployed in production, and which tools you would choose.
• Given a legacy AI deployment pipeline that frequently fails during rollout, how would you systematically troubleshoot and improve it?
• Imagine you are asked to implement a new feature that requires rapid scaling of AI inferencing services. How would you plan and prioritize your tasks?
• How do you keep up with rapidly evolving ML deployment technologies and decide which ones to adopt for your projects?
• Why does working at Splunk, particularly on their AI platform, excite you? How do you see your skills contributing to their mission?
• How do you align your work style and personal values with a company culture that emphasizes both customer impact and team success?
• What interests you most about working in a fully remote role, and how do you maintain productivity and connection with your team in such an environment?
• Can you describe how Lensa’s use of machine learning to connect job seekers and employers resonates with your understanding of AI’s potential?
• What questions do you have about Splunk’s AI platform team’s roadmap and how this role can influence long-term product innovation?