Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com
We are seeking a Machine Learning Engineer to contribute to our next level of growth and expansion.
Job DescriptionWe are seeking for a Machine Learning Engineer with strong expertise in MLOps and Data Engineering to join our team. This role involves working closely with Data Scientists and MLOps specialists to design, implement, and maintain machine learning systems and pipelines in a production environment. You will be responsible for supporting end-to-end ML workflows, ensuring scalability, reliability, and efficiency in deployed models.
The ideal candidate will have a solid understanding of the machine learning lifecycle, experience in AWS cloud services (particularly SageMaker), and the ability to build and maintain CI/CD pipelines for ML workloads.
What is this position about?
- Collaborate with Data Scientists to operationalize ML models and integrate them into production systems.
- Design, build, and maintain MLOps pipelines for model deployment, monitoring, and retraining.
- Develop and manage ETL workflows and data pipelines to support ML processes.
- Implement infrastructure as code solutions (preferably AWS CloudFormation or Terraform).
- Work with AWS services such as SageMaker, Lambda, Step Functions, DynamoDB, and others.
- Use Docker for containerization and manage cloud-based deployments.
- Perform queries on Snowflake as needed for data analysis and validation.
- Ensure best practices for scalability, performance, and security in ML systems.
- Take a leadership role in guiding technical decisions and mentoring when required.
- Degree in Computer Science, Engineering, or a related field, or equivalent experience.
- Strong proficiency in Python and PySpark.
- Hands-on experience with AWS cloud services.
- Experience with SageMaker.
- Solid understanding of infrastructure as code principles (CloudFormation preferred).
- Knowladge of MLOps concepts and the machine learning lifecycle
- Experience building and maintaining CI/CD pipelines.
- Experience with ETL processes.
- Familiarity with Docker for containerization.
- Basic working knowledge of Snowflake (a plus).
- Demonstrated leadership skills, including guiding technical work and defining tasks.
- Excellent problem-solving skills with the ability to work independently
- Strong collaboration and communication skills, comfortable working in global, multicultural teams.
- Highly proactive, self-motivated, and able to work with minimal supervision.
What about languages?
You will need excellent written and verbal English for clear and effective communication with the team.
How much experience must I have?
In order to thrive in this role, you must have at least 5+ years of experience in similar roles.
Our Perks and Benefits:
📚 Learning Opportunities:
Certifications in AWS (we are AWS Partners), Databricks, and Snowflake.
Access to AI learning paths to stay up to date with the latest technologies.
Study plans, courses, and additional certifications tailored to your role.
Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.
English lessons to support your professional communication.
Travel opportunities to attend industry conferences and meet clients.
👩🏫 Mentoring and Development:
Career development plans and mentorship programs to help shape your path.
🎁 Celebrations & Support:
Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.
Company-provided equipment.
⚖️ Flexible working options to help you strike the right balance.
Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.
Blend360 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Blend360 and has not been reviewed or approved by Blend360.
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Fair & Transparent Compensation — Pay is considered fair-to-good by many, and public salary postings for common data roles indicate competitive packages in numerous markets. Feedback suggests overall company sentiment aligns with acceptable compensation relative to peers in consulting and analytics.
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Flexible Benefits — Flexible and remote/hybrid work arrangements are consistently highlighted in official materials and role descriptions. Feedback suggests flexibility is a meaningful part of the total rewards experience.
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Retirement Support — A 401(k) with company match is part of the core package. Feedback suggests retirement offerings are standard and contribute to a complete benefits set.
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What We Do
Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics. Blend360 has two divisions: Data Science Solutions: We work at the intersection of data, technology and analytics. Talent Solutions: We live and breathe the digital and talent marketplace.
