Hello
Full remote is fine
Senior Machine Learning Engineer Data Science & Analytics
Contract Length: 6 18 Months
Location: Remote (U.S. preferred); Chicago candidates strongly preferred
________________________________________
Position Overview
Client is seeking a highly skilled Senior Machine Learning Engineer to join its Data Science & Analytics organization. This is a backend-focused machine learning engineering role centered around building and operationalizing scalable AI/ML systems in production environments.
This is not a pure research or data science position. The ideal candidate will have strong software engineering fundamentals and experience implementing machine learning-driven products and services at scale within cloud environments.
The engineer will work closely with Data Scientists, Data Engineers, and Architecture teams to productionize machine learning solutions powering personalization, recommendation systems, analytics platforms, chatbot interfaces, and operational intelligence applications across Hyatt's digital ecosystem.
The environment is highly dynamic and fast-paced, supporting approximately 20 active applications and services. Candidates must be comfortable operating in ambiguity, learning new concepts quickly, and independently driving solutions end-to-end.
________________________________________
What the Hiring Manager is Looking For
The hiring manager emphasized that foundational engineering strength, adaptability, and critical thinking are more important than exact tool matches.
Strong candidates will demonstrate:
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
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:
o Data ingestion
o Feature engineering
o Model integration
o Real-time inference
o Batch processing
o 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:
o Python
o SQL
o PySpark
o Docker
Strong AWS cloud experience
Experience with Google Cloud Platform (Google Cloud Platform)
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
o Team uses existing LLMs rather than building foundational models
Master's degree in Computer Science, Software Engineering, or related field
o Bachelor's degree + strong equivalent experience acceptable
________________________________________
Technical Environment
Core Technologies
Python
SQL
PySpark
Docker
AWS
Google Cloud Platform
ML/AI Focus Areas
Real-time personalization
Recommendation systems
Search platforms
Internal analytics tooling
Chat interfaces and AI-assisted workflows
________________________________________
Interview Process
Step 1
Hiring Manager Screen (30 40 Minutes)
Focus areas:
Communication
Technical depth
Learning agility
Project ownership
Ambiguity management
Step 2
3 4 Technical Interviews (45 Minutes Each)
Interview topics include:
System design
Backend architecture
Scenario-based problem solving
STAR methodology behavioral questions
ML systems implementation
Clarifying ambiguous requirements
Critical thinking and engineering tradeoffs
Candidates should be prepared to discuss:
End-to-end ownership
Scalability decisions
Production ML deployments
Collaboration with Data Scientists
Cloud architecture patterns
Ideal Candidate Summary
The ideal candidate is a strong backend software engineer with hands-on ML systems exposure who can independently build scalable production services in cloud environments. They should thrive in ambiguity, learn quickly, think systematically, and demonstrate strong ownership across architecture, development, deployment, and operational support of ML-powered applications.
Gopal Gupta
Technical Recruiter
E:
D:
A: 505 Knolle Court, Saint Augustine| FL 32092
Never miss an opportunity! Create an alert based on the job you applied for.
