Job Details

Cisco Meraki is revolutionizing the way IT administrators run their infrastructure by providing simple and secure cloud-managed solutions. With a large install base of customers and rich wide-ranging data sets, the potential for data analytics to improve business performance for both our customers and our own business is enormous.

About the role

The Data Science Infrastructure team is a growing group that works closely with executives and leaders across the company to support the development and alignment on our business strategy. We are looking for a Software Technical Lead, Machine Learning Engineer focusing on MLOps infrastructure to build a next generation cloud-based analytics platform to solve performance and connectivity issues in enterprise environments.

Meraki's cloud-managed model offers a unique opportunity to draw upon data from hundreds of thousands of networks and millions of access points deployed across our wide ranging customer base. The goal is to apply the rich telemetry data available from these devices and combine it with the AI and the cloud to build an analytics engine that can provide intuitive, yet detailed insights into the performance issues across our customer networks. Given the scale of Meraki's deployment, this provides a unique engineering opportunity to build an impactful solution that can help enhance our customer experience at large.

What Will You Do

• Help to define and implement the Cisco Network Platform data science infra team's AI/ML priorities while collaborating with product managers, AI architects, designers, user researchers and engineering partners.
• Explore, design and implement advanced ML Infrastructure framework and tools.
• Establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.
• Evaluate the performance of AI models and systems through meticulous testing, online and offline experimentation, and benchmarking.
• Use your ingenuity and creativity to resolve complicated and/or novel product and engineering challenges.
• Influence architectural decisions with a focus on security, scalability, and high-performance.
• Collaborate with data science and full stack teams across the Cisco Network Platform organization to define and build features across the product portfolio.
• Work with multi-functional partners to establish team priorities and lead those engagements.
• Mentor senior and mid-career team members by providing technical guidance.

What Skills You Possess

• Bachelors 12-plus years of related experience, or Masters 8-plus years of related experience, or PhD 5-plus years of related experience
• Core MLOps & Infrastructure Skills: End-to-End MLOps Pipelines, Model Deployment & Serving, Model Monitoring & Observability, CI/CD for MLOps.
• Cloud & Infrastructure: Cloud Platforms (AWS, GCP, or Azure), Containerization & Orchestration (Docker / K8s), Infrastructure as Code (IaC) – Terraform, CloudFormation, Networking & Security – VPCs, IAM, API Gateways, role-based access control (RBAC).
• Data & Feature Engineering: Data processing platforms like Apache Kafka, Flink, Spark, Kinesis, etc; data lakes like SQL/No SQL stores, Snowflake, etc and ML libraries such as Pandas, Scikit-Learn, Tensorflow, Keras, etc.
• Experience in working with GPU Scheduling and Orchestration architecture as well as debugging accelerators like GPU/TPU/etc.
• Experience maintaining scalable MLOps platforms and supporting production systems to minimize customer downtime.
• Strong written and verbal communication skills and excellent attention to detail and accuracy
• Problem Solving and Critical thinking with focus on reliability and incident management.

Bonus Points For

• LLMOps Experience – Experience with GenAI framework like LangChain, Jarvis, Amazon Bedrock etc.
• Edge AI & On-Device ML – Optimizing models for low-latency, high-performance inference

We encourage you to drop us a line even if you don't have all the points above. That's a lot of different areas of responsibility! We will help you pick them up because we believe that great engineers come from a diverse set of backgrounds.

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