Required Skills: Python, SQL, PySpark, Docker, AWS, GCP
Job Description
Core Responsibilities
- Design and implement scalable backend architectures supporting machine learning products
- Build and operationalize AI/ML services across the full product lifecycle:
- Data ingestion
- Feature engineering
- Model integration
- Real-time inference
- Batch processing
- Deployment and monitoring
- Partner closely with Data Scientists to productionize machine learning models
- Develop streaming and batch data processing workflows at scale
- Implement infrastructure-as-code and CI/CD deployment pipelines
- Enhance and maintain feature store workflows and ML data pipelines
- Optimize latency, scalability, and reliability of ML systems
- Build services supporting personalization, recommendation engines, search, analytics, and conversational AI experiences
- Collaborate with Data Engineering, Architecture, Governance, and Security teams
- Support cloud-native ML infrastructure within AWS and Google Cloud environments
- Contribute to system design discussions and technical architecture decisions
Required Technical Qualifications
Must-Have Skills
- 5+ years of software engineering experience implementing cloud-native product solutions
- Strong experience building backend systems supporting ML/algorithmic products
- Expertise with:
- Python
- SQL
- PySpark
- Docker
- Strong AWS cloud experience
- Experience with Google Cloud Platform (GCP)
- Experience building streaming and batch data architectures at scale
- Strong system design and backend architecture experience
- Experience operating in Agile environments
- Experience with DevOps and CI/CD practices
- Ability to handle ambiguity and rapidly changing requirements
- Strong communication and collaboration skills
Preferred / Nice-to-Have Skills
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Experience with SageMaker
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Understanding of feature stores
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Hospitality or personalization/recommendation system experience
-
Real-time ML inference and personalization systems
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Infrastructure-as-code implementation experience
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Experience supporting AI/LLM-enabled applications - Team uses existing LLMs rather than building foundational models
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Master’s degree in Computer Science, Software Engineering, or related field