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Machine Learning Solutions Architect (US-Remote)

Join phData, a dynamic and innovative leader in the modern data stack. We partner with major cloud data platforms like Snowflake, AWS, Azure, GCP, Fivetran, Pinecone, Glean and dbt to deliver cutting-edge services and solutions. We're committed to helping global enterprises overcome their toughest data challenges. 
 

phData is a remote-first global company with employees based in the United States, Latin America and India. We celebrate the culture of each of our team members and foster a community of technological curiosity, ownership and trust. Even though we're growing extremely fast, we maintain a casual, exciting work environment. We hire top performers and allow you the autonomy to deliver results.

Machine Learning Engineers are the Swiss army knives of machine learning. They’re ready for anything, and they bring all the tools to ensure that data science models see the light of day. They own the infrastructure and deployment plan—from making sure data science models can actually be built using customer data to deploying them into a production environment, and everything in between.  They provide thought leadership by recommending the right technologies and solutions for a given use case, from the application layer to infrastructure.  Machine Learning Engineers have the team leadership and coding skills (e.g. Python, Java, and Scala) to get their solutions into production — and to help ensure performance, security, scalability, and robust data integration.

As a Solutions Architect on our Machine Learning Engineering team, you are responsible for:

  • Designing and implementing data solutions best suited to deliver on our customer needs — from model inference, retraining, monitoring, and beyond — across an evolving technical stack.
  • Providing thought leadership by recommending the technologies and solution design for a given use case, from the application layer to infrastructure; and they have the team leadership and coding skills (e.g. Python, Java, and Scala) to build and operate in production; and to help ensure performance, security, scalability, and robust data integration.

What you’ll do in this role:

  • Design and create environments for data scientists to build models and manipulate data
  • Work within customer systems to extract data and place it within an analytical environment
  • Learn and understand customer technology environments and systems
  • Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop
  • Demonstrate the business value of data by working with data scientists to manipulate and transform data into actionable insights
  • Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models
  • Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines, and such that the solution can be maintained throughout its life cycle
  • Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment
  • Ensure the quality of the delivered product

This job might be for you if you bring...

  • At least 6 years experience as a Machine Learning Engineer, Software Engineer, or Data Engineer
  • 4-year Bachelor's degree in Computer Science or a related field
  • Experience deploying machine learning models in a production setting
  • Expertise in Python, Scala, Java, or another modern programming language
  • The ability to build and operate robust data pipelines using a variety of data sources,  programming languages, and toolsets
  • Strong working knowledge of SQL and the ability to write, debug, and optimize distributed SQL queries
  • Hands-on experience in one or more big data ecosystem products/languages such as Spark, Snowflake, Databricks, etc. 
  • Familiarity with multiple data sources (e.g. JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP)
  • Systems-level knowledge in network/cloud architecture, operating systems (e.g., Linux), and storage systems (e.g., AWS, Databricks, Cloudera)
  • Production experience in core data technologies (e.g. Spark, HDFS, Snowflake, Databricks, Redshift, & Amazon EMR)
  • Development of APIs and web server applications (e.g. Flask, Django, Spring)
  • Complete software development lifecycle experience, including design, documentation, implementation, testing, and deployment
  • Excellent communication and presentation skills; previous experience working with internal or external customers

You might also have...

  • A Master’s or other advanced degree in data science or a related field
  • Hands-on experience with one or more ecosystem technologies (e.g., Spark, Databricks, Snowflake, AWS/Azure/GCP)
  • Relevant side projects (e.g. contributions to an open source technology stack)
  • Experience working with Data-Science and Machine-Learning software and libraries such as h2o, TensorFlow, Keras, scikit-learn, etc. 
  • Experience with Docker, Kubernetes, or some other containerization technology
  • AWS Sagemaker (or Azure ML) and MLflow experience
  • Experience building enterprise ML models

Why phData? We offer:

  • Remote-First Work Environment 
  • Casual, award-winning small-business work environment
  • Collaborative culture that prizes autonomy, creativity, and transparency
  • Competitive comp, excellent benefits, 4 weeks of PTO plus 10 Holidays (and other cool perks)
  • Accelerated learning and professional development through advanced training and certifications

phData celebrates diversity and is committed to creating an inclusive environment for all employees. Our approach helps us to build a winning team that represents a variety of backgrounds, perspectives, and abilities. So, regardless of how your diversity expresses itself, you can find a home here at phData. We are proud to be an equal opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, religion, national origin, sex (including pregnancy), sexual orientation, gender identity, gender expression, age, veteran status, genetic information, disability, or other applicable legally protected characteristics. If you would like to request an accommodation due to a disability, please contact us at People Operations.

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Average salary estimate

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What You Should Know About Machine Learning Solutions Architect (US-Remote), phData

If you’re a talented Machine Learning Solutions Architect looking to make an impact, look no further than phData! We're a pioneering force in the modern data stack, collaborating with top-tier cloud data platforms such as Snowflake, AWS, Azure, GCP, and many more. As a Solutions Architect on our Machine Learning Engineering team, you will play a critical role in designing and implementing innovative data solutions that meet our customer needs. Your responsibilities will include creating robust environments for data scientists and overseeing the deployment of transformative machine learning models. You will also have the opportunity to work with advanced technologies, providing thought leadership and ensuring the best practices in data integration and model performance. With your extensive experience, you'll bring the coding prowess needed to architect scalable solutions and guide your team through complexities with confidence and creativity. At phData, we value a remote-first culture that prioritizes autonomy and curiosity, all while celebrating the unique backgrounds and perspectives of our team members. If you're passionate about harnessing data to drive significant impact and enjoy a supportive, casual work environment, you’ll fit right in at phData!

Frequently Asked Questions (FAQs) for Machine Learning Solutions Architect (US-Remote) Role at phData
What are the primary responsibilities of a Machine Learning Solutions Architect at phData?

As a Machine Learning Solutions Architect at phData, your primary responsibilities include designing and implementing effective data solutions that support our customer needs. You will focus on deploying machine learning models, building environments for data scientists, and ensuring robust data integration and model performance, ultimately enabling businesses to leverage data for actionable insights.

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What qualifications are needed for the Machine Learning Solutions Architect position at phData?

To qualify for the Machine Learning Solutions Architect role at phData, candidates should have at least 6 years of experience in roles such as Machine Learning Engineer, Software Engineer, or Data Engineer. A Bachelor’s degree in Computer Science or a related field is essential, along with expertise in programming languages like Python, Java, or Scala, and familiarity with big data technologies like Spark and Snowflake.

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How does phData ensure the success of its Machine Learning Solutions Architects?

At phData, we believe in providing our Machine Learning Solutions Architects with the autonomy and support they need to succeed. We foster a collaborative culture that encourages continuous learning and professional development, offering access to advanced training and certifications. Our commitment to innovation ensures that our architects can thrive and contribute effectively to client projects.

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What programming skills are essential for a Machine Learning Solutions Architect at phData?

Essential programming skills for a Machine Learning Solutions Architect at phData include proficiency in Python, Scala, and Java. Familiarity with SQL for managing and optimizing queries is also crucial. Candidates should also have experience building robust data pipelines across various data sources and technologies to ensure efficient model deployment and execution.

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What makes phData a unique workplace for a Machine Learning Solutions Architect?

phData distinguishes itself as a unique workplace for a Machine Learning Solutions Architect by fostering an inclusive, remote-first culture that values diversity and creativity. Our commitment to maintaining a casual yet impactful work environment allows you to thrive while contributing to projects that push the boundaries of data technology and collaborative problem-solving.

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Common Interview Questions for Machine Learning Solutions Architect (US-Remote)
Can you describe your experience with deploying machine learning models in production?

When discussing your experience with deploying machine learning models in production, focus on specific projects where you have managed the entire lifecycle—from design to deployment. Highlight any challenges you faced, how you addressed them, and any technologies or frameworks you used, such as TensorFlow, Docker, or Kubernetes, to ensure smooth deployment.

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What are key considerations for ensuring the scalability of machine learning solutions?

When answering this question, emphasize the importance of designing systems with scalability in mind, particularly focusing on distributed computing, efficient data pipeline architecture, and cloud-based solutions. Discuss your experience with big data technologies like Spark and how you have implemented solutions to handle larger datasets efficiently.

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How do you approach recommending technology solutions for a specific use case?

Share your structured approach to analyzing customer requirements and existing systems before making recommendations. Discuss how you leverage both technical expertise and communication skills to advise clients on the best technologies that align with their needs, keeping factors like performance, cost, and scalability in mind.

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Describe a time when you faced a challenge in a machine learning project and how you overcame it.

Utilize the STAR method (Situation, Task, Action, Result) to structure your response. Detail the specific challenge, what was at stake, the steps you took to overcome it, and the positive outcome that resulted from your efforts. Showing resilience and problem-solving abilities is key.

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How do you ensure data quality when developing machine learning solutions?

Emphasize the importance of data validation and cleansing processes in your answer. Discuss methodologies like A/B testing and cross-validation to ensure the accuracy and reliability of your models, as well as leveraging monitoring tools to continually assess data quality.

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What experiences do you have with big data technologies?

Share specific technologies you have worked with, such as Snowflake, Databricks, or Hadoop. Elaborate on how you have utilized these tools in previous projects, detailing your role and the impact of these technologies on the project's success.

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How would you approach collaboration with data scientists to deploy solutions?

Your answer should reflect your understanding of the collaborative nature of the role. Discuss strategies for effective communication, regular check-ins, and joint brainstorming sessions that can lead to better alignment between data scientists and architects in deploying solid, scalable solutions.

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Can you explain your experience with data pipelines?

In your answer, discuss specific data pipeline architectures you have implemented, the challenges faced, and the tools used such as Apache Kafka or AWS Glue. Highlight your understanding of data ingestion, transformation, and storage strategies to ensure efficient data flow.

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What role do APIs play in machine learning solutions, and how have you utilized them?

Explain the significance of APIs in enabling integration and communication between machine learning models and applications. Share real-world examples of how you've developed APIs for model consumption, discussing any frameworks you've used, such as Flask or Django.

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How do you keep current with the latest advancements in machine learning and data technologies?

Discuss your approach to continuous learning, whether through online courses, attending industry conferences, engaging in professional communities, or contributing to open-source projects. Show your commitment to staying updated with the latest trends and technologies in the machine learning field.

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Full-time, remote
DATE POSTED
April 3, 2025

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