📍 Job Overview

Job Title: MLOps Engineer (Infra)
Company: Sunbit
Location: Binyamina - Givat Ada, Haifa District, Israel
Job Type: On-site
Category: DevOps Engineer
Date Posted: 2025-08-03
Experience Level: 5-10 years

🚀 Role Summary

Key Responsibilities: Design, implement, and enhance robust and scalable infrastructure for machine learning models. Streamline data and feature pipelines, optimize model serving, and ensure governance and reproducibility across the ML lifecycle.
Key Technologies: MLOps, Machine Learning, Python, SQL, Dask, Spark, Kubernetes, Docker, AWS, Airflow, MLflow, Kafka, CI/CD, Monitoring, Data Pipelines, Feature Stores

📝 Enhancement Note: This role requires a strong background in MLOps and a solid understanding of machine learning infrastructure to drive efficient deployment, monitoring, and management of models in production.

💻 Primary Responsibilities

Infrastructure Design & Implementation: Design, implement, and enhance robust and scalable infrastructure for machine learning models.
Data Pipeline Streamlining: Decouple data prep from model training to accelerate experimentation and deployment. Build efficient data workflows with versioning, lineage, and optimized resource use.
Model Deployment & Monitoring: Automate and standardize model deployment with pre-deployment testing. Monitor model performance, detect drift, and trigger alerts across the ML lifecycle.
Feature Store Management: Manage a unified feature store with history, drift detection, and centralized feature/label tracking. Establish a single source of truth for features across research and production.
Collaboration & Knowledge Sharing: Work closely with interdisciplinary teams to drive innovative solutions and influence company-wide technology decisions.

📝 Enhancement Note: This role requires a balance of technical expertise and strong communication skills to collaborate effectively with various teams and stakeholders.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field. Relevant experience may be considered in lieu of a degree.

Experience: 3+ years of experience as an MLOps, ML Infrastructure, or Software Engineer in ML-driven environments, preferably with PyTorch.

Required Skills:

Strong proficiency in Python, SQL (leveraging platforms like Snowflake and RDS), and distributed computing frameworks (e.g., Dask, Spark) for processing large-scale data in formats like Parquet.
Hands-on experience with feature stores, key-value stores like Redis, MLflow (or similar tools), Kubernetes, Docker, cloud infrastructure (AWS, specifically S3 and EC2), and orchestration tools (Airflow).
Proven ability to build and maintain scalable and version-controlled data pipelines, including real-time streaming with tools like Kafka.
Experience in designing and deploying robust ML serving infrastructures with CI/CD automation.
Familiarity with monitoring tools and practices for ML systems, including drift detection and model performance evaluation.

Preferred Skills:

Experience with GPU optimization frameworks and distributed training.
Familiarity with advanced ML deployments, including NLP and embedding models.
Knowledge of data versioning tools (e.g., DVC) and infrastructure-as-code practices.
Prior experience implementing structured A/B testing or dark mode deployments for ML models.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience in designing, implementing, and managing machine learning infrastructure projects.
Showcase data pipeline projects that exhibit versioning, lineage, and optimized resource use.
Present model deployment projects with pre-deployment testing and monitoring components.
Highlight feature store management projects with history, drift detection, and centralized feature/label tracking.

Technical Documentation:

Provide clear and concise documentation for your machine learning infrastructure projects, including data pipelines, model deployment, and feature store management.
Include version control, deployment processes, and server configuration details in your technical documentation.
Demonstrate understanding of testing methodologies, performance metrics, and optimization techniques in your project documentation.

📝 Enhancement Note: As an MLOps Engineer, your portfolio should emphasize your ability to streamline data workflows, optimize model serving, and ensure governance and reproducibility across the ML lifecycle.

💵 Compensation & Benefits

Salary Range: The estimated salary range for this role in Israel is ₪35,000 - ₪55,000 per month, based on experience and market standards for MLOps Engineers. This estimate is derived from regional salary data and web technology industry benchmarks.

Benefits:

Competitive compensation package
Comprehensive health insurance
Retirement savings plan
Generous vacation and leave policies
Opportunities for professional development and growth

Working Hours: Full-time position with standard office hours. Flexible working hours may be available for specific projects or tasks.

📝 Enhancement Note: The salary range provided is an estimate and should be verified with the hiring organization. Benefits may vary based on the company's benefits package and regional offerings.

🎯 Team & Company Context

Company Culture:

Industry: Sunbit operates in the financial technology industry, focusing on providing accessible and fair payment options for consumers.
Company Size: Sunbit is a growing organization with a strong focus on innovation and collaboration. As of 2022, it was included on the Inc. 5000 list and has been recognized as a Most Loved Workplace®, Best Point of Sale Company, and a Top Fintech Startup by CB Insights.
Founded: Sunbit was founded in 2016, with a mission to ease the stress of paying for life's expenses by giving people more options on how and when they pay.

Team Structure:

The Data/ML Infrastructure team is a central component of the Infrastructure group, responsible for cross-company technology initiatives. This specialized team focuses on data infrastructure, AI/ML infrastructure, and all related systems that empower Sunbit's diverse business functions.
As an MLOps Engineer, you will work closely with interdisciplinary teams, including data scientists, software engineers, and other infrastructure specialists, to drive innovative solutions and influence company-wide technology decisions.

Development Methodology:

Sunbit follows Agile methodologies for software development, with a focus on continuous integration, delivery, and improvement.
The company encourages collaboration, innovation, and a culture of learning to drive success in the rapidly evolving financial technology industry.

Company Website: Sunbit

📝 Enhancement Note: Sunbit's company culture emphasizes innovation, collaboration, and a strong focus on user experience. This context is essential for understanding the company's approach to machine learning infrastructure and the role's impact on its products and services.

📈 Career & Growth Analysis

Web Technology Career Level: This MLOps Engineer (Infra) role is a senior-level position that requires a deep understanding of machine learning infrastructure and a proven track record of driving efficient deployment, monitoring, and management of models in production.

Reporting Structure: As an MLOps Engineer, you will report directly to the Data/ML Infrastructure team lead and collaborate closely with various teams, including data scientists, software engineers, and other infrastructure specialists.

Technical Impact: In this role, you will have a significant impact on Sunbit's machine learning systems, driving reliability, scalability, and efficiency across the ML lifecycle. Your work will enable the company to provide accessible and fair payment options to a broader range of consumers.

Growth Opportunities:

Technical Growth: Continue to develop your expertise in MLOps, machine learning infrastructure, and related technologies. Explore emerging trends in the field and contribute to Sunbit's cutting-edge innovations.
Leadership Development: As Sunbit grows, there may be opportunities to take on more significant roles within the Data/ML Infrastructure team or mentor junior team members.
Architecture Decisions: As an experienced MLOps Engineer, you will have the opportunity to influence architecture decisions and help shape Sunbit's machine learning infrastructure.

📝 Enhancement Note: Sunbit's focus on innovation and collaboration creates an environment where MLOps Engineers can grow technically and professionally. The company's rapid growth also presents opportunities for taking on more significant roles and responsibilities.

🌐 Work Environment

Office Type: Sunbit's office is a modern, collaborative workspace designed to foster innovation and creativity. The company encourages open communication and cross-functional collaboration among its teams.

Office Location(s): Sunbit's headquarters is located at 58 Ha-Takhana Street, Binyamina - Givat Ada, Haifa District, Israel.

Workspace Context:

Collaborative Environment: Sunbit's office features open workspaces, meeting rooms, and breakout areas designed to encourage collaboration and communication among team members.
Development Tools: The company provides access to modern development tools, including multiple monitors, testing devices, and other resources to support the work of its MLOps Engineers.
Cross-Functional Collaboration: As an MLOps Engineer, you will work closely with various teams, including data scientists, software engineers, and other infrastructure specialists. This collaboration will enable you to gain a deep understanding of Sunbit's products and services and contribute to their success.

Work Schedule: Sunbit offers a flexible work schedule, with standard office hours and the option to work from home for specific projects or tasks. The company also provides generous vacation and leave policies to support work-life balance.

📝 Enhancement Note: Sunbit's work environment is designed to foster collaboration, innovation, and a strong focus on user experience. This context is essential for understanding the company's approach to machine learning infrastructure and the role's impact on its products and services.

📄 Application & Technical Interview Process

Interview Process:

Technical Phone Screen: A brief phone or video call to assess your technical skills and understanding of MLOps and machine learning infrastructure.
On-site Technical Interview: A more in-depth discussion of your technical expertise, focusing on your experience with data pipelines, model deployment, and feature store management. This interview may include coding challenges or architecture discussions.
Behavioral Interview: An assessment of your problem-solving skills, communication abilities, and cultural fit within Sunbit's teams.
Final Evaluation: A review of your technical skills, cultural fit, and potential impact on Sunbit's machine learning systems.

Portfolio Review Tips:

Project Selection: Choose projects that demonstrate your experience in designing, implementing, and managing machine learning infrastructure. Highlight data pipeline projects, model deployment projects, and feature store management projects.
Documentation: Ensure your project documentation is clear, concise, and well-organized. Include version control, deployment processes, and server configuration details.
Presentation: Prepare a live demo or presentation of your projects, focusing on the technical aspects and the impact they had on the machine learning lifecycle.

Technical Challenge Preparation:

Data Pipeline Challenges: Familiarize yourself with data pipeline tools and frameworks, such as Dask, Spark, and Airflow. Practice building efficient data workflows with versioning, lineage, and optimized resource use.
Model Deployment Challenges: Brush up on your knowledge of model deployment tools, such as MLflow, and pre-deployment testing strategies. Practice automating and standardizing model deployment processes.
Feature Store Challenges: Review your experience with feature store management tools and practices. Prepare for questions about centralized feature/label tracking, drift detection, and establishing a single source of truth for features across research and production.

ATS Keywords: (Organized by category)

Programming Languages: Python, SQL, Bash, JavaScript
Web Frameworks: MLflow, Airflow, Kubernetes, Docker
Server Technologies: AWS (S3, EC2), Snowflake, Redis
Databases: PostgreSQL, MySQL, MongoDB
Tools: Dask, Spark, Kafka, Jenkins, Git, GitHub
Methodologies: Agile, CI/CD, Infrastructure as Code, MLOps
Soft Skills: Problem-solving, Communication, Collaboration, Teamwork
Industry Terms: Machine Learning, Deep Learning, Neural Networks, Feature Store, Data Pipeline, Model Deployment, Drift Detection, A/B Testing

📝 Enhancement Note: Sunbit's interview process focuses on assessing your technical expertise and cultural fit within the company's teams. By preparing for the interview process with a strong understanding of machine learning infrastructure and relevant technologies, you can demonstrate your qualifications for the MLOps Engineer (Infra) role.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: (Not applicable for this role)

Backend & Server Technologies:

Machine Learning Frameworks: PyTorch, TensorFlow, Scikit-learn
Cloud Infrastructure: AWS (S3, EC2), Google Cloud Platform, Microsoft Azure
Containerization: Docker, Kubernetes
Orchestration: Airflow, Prefect
Data Processing: Dask, Spark, Pandas, NumPy
Data Storage: Snowflake, PostgreSQL, MongoDB, Redis
Version Control: Git, GitHub
CI/CD: Jenkins, GitHub Actions, CircleCI
Monitoring: Prometheus, Grafana, ELK Stack

📝 Enhancement Note: Sunbit's technology stack is designed to support the development and deployment of machine learning models at scale. As an MLOps Engineer (Infra), you will work with these technologies to streamline data workflows, optimize model serving, and ensure governance and reproducibility across the ML lifecycle.

👥 Team Culture & Values

Web Development Values:

Innovation: Sunbit encourages its team members to explore new technologies and approaches to drive innovation in the financial technology industry.
Collaboration: The company fosters a culture of collaboration, with open communication and cross-functional teamwork.
User-Centric: Sunbit places a strong emphasis on understanding and addressing the needs of its users, providing accessible and fair payment options.
Continuous Learning: The company encourages its team members to stay up-to-date with the latest trends and best practices in machine learning and financial technology.

Collaboration Style:

Cross-Functional Integration: Sunbit's teams work closely together, with data scientists, software engineers, and infrastructure specialists collaborating to drive innovative solutions.
Code Review Culture: The company encourages a culture of code review, with peer programming and pair programming practices to ensure high-quality work and knowledge sharing.
Knowledge Sharing: Sunbit fosters a culture of knowledge sharing, with regular team meetings, workshops, and training sessions to help team members develop their skills and stay up-to-date with the latest trends in machine learning and financial technology.

📝 Enhancement Note: Sunbit's team culture is built on innovation, collaboration, and a strong focus on user experience. This context is essential for understanding the company's approach to machine learning infrastructure and the role's impact on its products and services.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Scalability: As Sunbit grows, you will be challenged to scale its machine learning infrastructure to support an increasing number of users and models.
Performance Optimization: You will be responsible for optimizing data pipelines, model serving, and feature store management to ensure Sunbit's machine learning systems can handle the demands of a growing user base.
Emerging Technologies: Sunbit encourages its team members to explore emerging trends in machine learning and financial technology. As an MLOps Engineer (Infra), you may be challenged to integrate new technologies into the company's infrastructure.

Learning & Development Opportunities:

Technical Skill Development: Sunbit offers opportunities for technical skill development, with workshops, training sessions, and access to relevant conferences and events.
Mentorship: The company provides mentorship opportunities, with senior team members sharing their knowledge and experience to help junior team members grow professionally.
Leadership Development: As Sunbit grows, there may be opportunities for MLOps Engineers to take on more significant roles within the Data/ML Infrastructure team or mentor junior team members.

📝 Enhancement Note: Sunbit's challenges and growth opportunities are designed to drive innovation, collaboration, and a strong focus on user experience. By embracing these challenges and pursuing continuous learning and development, MLOps Engineers can make a significant impact on the company's machine learning systems and products.

💡 Interview Preparation

Technical Questions:

Data Pipeline Questions: Be prepared to discuss your experience with data pipeline tools and frameworks, such as Dask, Spark, and Airflow. Practice building efficient data workflows with versioning, lineage, and optimized resource use.
Model Deployment Questions: Brush up on your knowledge of model deployment tools, such as MLflow, and pre-deployment testing strategies. Practice automating and standardizing model deployment processes.
Feature Store Questions: Review your experience with feature store management tools and practices. Prepare for questions about centralized feature/label tracking, drift detection, and establishing a single source of truth for features across research and production.

Company & Culture Questions:

Sunbit's Approach to Machine Learning: Research Sunbit's approach to machine learning infrastructure and be prepared to discuss how your experience aligns with the company's goals and values.
Collaboration & Teamwork: Prepare for questions about your experience working in a collaborative environment and your ability to work effectively with cross-functional teams.
User Experience Focus: Understand Sunbit's focus on user experience and be prepared to discuss how your work as an MLOps Engineer (Infra) will contribute to the company's mission of providing accessible and fair payment options.

Portfolio Presentation Strategy:

Project Selection: Choose projects that demonstrate your experience in designing, implementing, and managing machine learning infrastructure. Highlight data pipeline projects, model deployment projects, and feature store management projects.
Documentation: Ensure your project documentation is clear, concise, and well-organized. Include version control, deployment processes, and server configuration details.
Presentation: Prepare a live demo or presentation of your projects, focusing on the technical aspects and the impact they had on the machine learning lifecycle.

📝 Enhancement Note: Sunbit's interview process focuses on assessing your technical expertise and cultural fit within the company's teams. By preparing for the interview process with a strong understanding of machine learning infrastructure and relevant technologies, you can demonstrate your qualifications for the MLOps Engineer (Infra) role.

📌 Application Steps

To apply for this MLOps Engineer (Infra) position at Sunbit:

Submit Your Application: Click on the application link and submit your resume, highlighting your relevant experience and skills.
Customize Your Portfolio: Tailor your portfolio to showcase your experience in designing, implementing, and managing machine learning infrastructure. Highlight data pipeline projects, model deployment projects, and feature store management projects.
Optimize Your Resume: Ensure your resume is optimized for web technology roles, with a focus on project highlights and technical skills relevant to the MLOps Engineer (Infra) position.
Prepare for Technical Challenges: Brush up on your knowledge of data pipeline tools, model deployment tools, and feature store management tools. Practice building efficient data workflows, automating model deployment processes, and managing feature store projects.
Research Sunbit: Learn about Sunbit's approach to machine learning infrastructure and its focus on innovation, collaboration, and user experience. Prepare for questions about your experience and how it aligns with the company's goals and values.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web technology industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

MLOps Engineer (Infra)