ml engineer for personalization platforms

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• Collaborate with Data Scientists to adapt, package, and deploy 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 and egress connectivity
• Define and manage infrastructure using Terraform and establish a simple CI/CD pipeline
• Implement observability and monitoring for latency, errors, 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
• Present technical results and business impact to technical and non-technical stakeholders

• 5+ Years of experience in ML/AI engineering, building and serving recommendation or ranking models, including offline model evaluation
• At least 1 year of relevant leadership experience
• Hands-on knowledge of Collaborative Filtering, Two-Tower Architectures, and Learning-to-Rank approaches
• Strong proficiency in Python and production experience building services using FastAPI
• Understanding of asynchronous processing, connection pooling, and caching strategies
• Expertise in timeout handling, fail-open mechanisms, and resilient service design
• Experience registering, versioning, and serving ML models with MLflow and Databricks Model Serving
• Familiarity with Service Principal authentication, endpoint scaling, and version management
• Experience monitoring latency, errors, and fallback rates, including creating alerts and dashboards
• Skills in load-testing tools such as k6 or Locust, with the ability to validate p99 latency targets
• Ability to explain technical concepts and outcomes in business terms and work with engineering, data science, and business stakeholders
• English proficiency at Upper-Intermediate level (B2) or higher
• Будет плюсом: Terraform and CI/CD pipeline implementation sufficient to deploy and hand over a reproducible POC environment, AWS API Gateway and Lambda integration including cold starts and performance optimization, VPC networking and egress connectivity to Databricks, OpenAPI-first REST API design and contract testing using Pact or schema-based testing approaches, designing services for multi-region deployment and future scalability, GenAI for personalization including embeddings, vector search, LLM-based re-ranking, and personalized explanations powered by GenAI

• Remote hiring locations: Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan