Lead ML/AI Solution Engineer with Databricks

Responsibilities

• Collaborate with Data Scientists to adapt, package, and deploy personalization models (recommendation and ranking models) to Databricks Model Serving using MLflow
• Build and maintain a FastAPI service that invokes one or more model serving endpoints
• Implement production-grade engineering practices, including caching, connection pooling, request timeouts, and fail-open fallback mechanisms
• Configure secure authentication between services and Databricks using Service Principals
• Set up and maintain the AWS integration layer, including API Gateway, AWS Lambda, and networking/egress connectivity
• Define and manage infrastructure using Terraform and establish a simple CI/CD pipeline
• Implement observability and monitoring, including latency, error, and fallback metrics
• Conduct load and performance testing to validate system behavior under expected traffic volumes
• Define REST API contracts and implement contract testing to enable early integration by consuming teams
• Perform offline evaluation of personalization models and support online validation through A/B testing or shadow testing where feasible

Languages

Required

• 5+ years of experience in ML/AI engineering, building and serving recommendation or ranking models, including offline model evaluation
• Strong proficiency in Python with production experience building services using FastAPI
• Experience registering, versioning, and serving ML models with MLflow and Databricks Model Serving
• Skills in load-testing tools such as k6 or Locust, with the capability to validate p99 latency targets

Nice to have

• At least 1 year of relevant leadership experience
• English proficiency at an Upper-Intermediate level (B2) or higher
• Proficiency in Infrastructure-as-Code using Terraform and CI/CD pipeline implementation sufficient to deploy and hand over a reproducible POC environment
• Experience with AWS API Gateway and Lambda integration, including understanding of cold starts and performance optimization
• Knowledge of VPC networking and egress connectivity to Databricks
• Competency in OpenAPI-first REST API design and contract testing using Pact or schema-based testing approaches
• Familiarity with GenAI for personalization, including embeddings, vector search, LLM-based re-ranking, and personalized explanations powered by GenAI

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