The Role
Lead design and evolution of enterprise ML/AI platforms, architecting Databricks-based feature stores, model registries, vector search and model serving. Optimize distributed Spark workloads, enable RAG workflows, operationalize GenAI, drive automation and governance, and mentor engineering teams to productionize scalable AI solutions.
Summary Generated by Built In
As a Principal MLOps Platform Engineer, you will:
- Lead the evolution of enterprise Machine Learning and AI platforms
- Design scalable platform architecture for AI Agents and Vector Database implementations
- Evaluate and operationalize new platform capabilities and emerging Databricks features
- Establish engineering standards and governance for production-grade GenAI applications
- Design and manage Feature Stores, Model Registries, and Vector Search infrastructure
- Enable Retrieval Augmented Generation workflows across enterprise AI use cases
- Mentor senior engineering teams on platform design patterns, code quality, and architecture
- Translate AI and ML engineering requirements into robust engineering roadmaps
- Drive automation, scalability, and operational excellence across AI infrastructure
- Collaborate with cross-functional teams to enable production-ready AI solutions
What You Bring to the Table:
- 10+ years of experience across Data Engineering, MLOps, and AI Infrastructure
- Deep expertise with Databricks AI Stack including MLflow, Mosaic AI, Unity Catalog, Feature Store, Model Serving, and Vector Databases
- Advanced knowledge of Apache Spark internals, structured streaming, and performance optimization
- Strong experience with AWS infrastructure including EC2 and GPU-based compute environments
- Expert-level Python programming skills with hands-on PyTorch experience
- Strong experience with Terraform and Terragrunt for infrastructure automation
- Hands-on expertise with Docker and Kubernetes
- Experience building CI/CD pipelines using GitHub Actions
- Strong observability and monitoring experience using Datadog
- Proven experience operationalizing traditional ML and Generative AI workloads
You Should Possess the Ability to:
- Architect and scale enterprise-grade ML and AI platforms
- Optimize distributed compute workloads for large-scale processing
- Build low-latency, high-concurrency model serving systems
- Bridge experimental ML development with production-grade engineering
- Drive technical leadership and mentor engineering teams
- Translate business and AI requirements into scalable technical solutions
What We Bring to the Table:
- Opportunity to build cutting-edge enterprise AI and GenAI platforms
- Exposure to advanced Databricks, MLOps, and cloud-native technologies
- A highly collaborative and innovation-driven engineering environment
- Opportunities to shape enterprise AI strategy and technical standards
Let’s Connect
Want to discuss this opportunity in more detail? Feel free to reach out.
Recruiter: Asha Krishnan
Phone: +31 20 369 0609 ; Extn :146
LinkedIn: https://www.linkedin.com/in/asha-krishnan
Skills Required
- 10+ years experience across Data Engineering, MLOps, and AI Infrastructure
- Deep expertise with Databricks AI Stack (MLflow, Mosaic AI, Unity Catalog, Feature Store, Model Serving, Vector Databases)
- Advanced knowledge of Apache Spark internals, structured streaming, and performance optimization
- Strong experience with AWS infrastructure including EC2 and GPU-based compute environments
- Expert-level Python programming skills with hands-on PyTorch experience
- Strong experience with Terraform and Terragrunt for infrastructure automation
- Hands-on expertise with Docker and Kubernetes
- Experience building CI/CD pipelines using GitHub Actions
- Strong observability and monitoring experience using Datadog
- Proven experience operationalizing traditional ML and Generative AI workloads
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The Company
What We Do
STAFIDE is a Netherlands-based niche technology talent consulting company operating across Europe. It helps organizations identify, recruit, and deploy technology professionals in areas including cybersecurity, cloud engineering, software development, data analytics, ERP, infrastructure, and digital transformation. Its services include recruitment, workforce engagement, secondment, onboarding support, workforce deployment, and workforce analytics that support technology hiring and expansion.
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