Senior Machine Learning Engineer - Data Science & Analytics

Candidates with pure data science or research-heavy backgrounds are less aligned unless they possess strong production engineering experience. 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, * Python

• SQL

• PySpark

• Docker

• AWS

• GCP

ML/AI Focus Areas

• Real-time personalization
• Recommendation systems
• Search platforms
• Internal analytics tooling
• Chat interfaces and AI-assisted workflows

• Exceptional software engineering and computer science fundamentals

• Experience building scalable backend systems supporting ML workloads

• Ability to architect, deploy, and maintain production-grade AI/ML services

• Comfort working in ambiguous and evolving environments

• Strong analytical and systematic problem-solving skills

• Fast learning ability and intellectual curiosity

• Experience collaborating cross-functionally with Data Scientists and Engineering teams

• Proven delivery experience in enterprise or high-scale technology environments, * 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

• Experience with SageMaker

• Understanding of feature stores

• Hospitality or personalization/recommendation system experience

• Real-time ML inference and personalization systems

• Infrastructure-as-code implementation experience

• Experience supporting AI/LLM-enabled applications

• Team uses existing LLMs rather than building foundational models

• Master's degree in Computer Science, Software Engineering, or related field

• Bachelor's degree + strong equivalent experience acceptable

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