Sr. Machine Learning Solutions Architect

Remote from: LATAM
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 12 Jun 2026
Apply before: 12 Jul 2026
Experience level: Senior
Views / Applies: 14 / 1

phData is the leading AI & data services company partnering with the world's top brands to execute data initiatives in artificial intelligence, data engineering, applications, analytics, and cloud platform operations.

AI Summary

phData is seeking a Sr. Machine Learning Solutions Architect to lead the architecture and delivery of production-grade ML solutions for enterprise clients. The role involves end-to-end ownership of solution design, collaboration with cross-functional teams, and ensuring high-quality delivery. The ideal candidate will have deep expertise in ML, MLOps, and cloud platforms. This is a remote-first position with a fast-paced, autonomous work environment. phData is a recognized leader in the modern data stack with multiple partner awards.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role requires deep technical expertise in ML, MLOps, and cloud platforms, combined with client-facing leadership and delivery responsibilities, placing it at a high difficulty level.

Salary Analysis

AI Insight The salary range is not provided, but based on market data for senior ML Solutions Architects in the US, the estimated median is $175,000, which is competitive for the role and responsibilities described.

Key Skills

Machine Learning MLOps AWS Azure GCP Snowflake Data Engineering Solutions Architecture Python Data Science

Cover Letter Sample

I am writing to express my strong interest in the Senior Machine Learning Solutions Architect position at phData. With extensive experience in designing and delivering production ML solutions, I have a proven track record of driving business value for enterprise clients through scalable architectures and MLOps best practices. My expertise spans cloud platforms like AWS and GCP, as well as familiarity with Snowflake and dbt, aligning perfectly with phData's technology partners.

I am particularly drawn to phData's innovative culture and commitment to helping enterprises overcome data challenges. In my current role, I have successfully led cross-functional teams to implement end-to-end ML pipelines from data extraction to model deployment and monitoring. I am confident in my ability to provide the technical leadership and strategic guidance that phData expects from this senior role.

I look forward to the opportunity to discuss how my skills and experiences can contribute to phData's continued success and growth. Thank you for your consideration.

Possible Interview Questions

Describe your experience designing and deploying production-grade ML systems. What key considerations do you address for scalability and reliability?

I have designed ML systems that handle millions of predictions per day. Key considerations include: using feature stores for consistent feature engineering, implementing automated retraining pipelines, monitoring model drift, and ensuring robust inference infrastructure with load balancing and fallback mechanisms. I also emphasize CI/CD for ML models, versioning of data and models, and comprehensive logging for debugging.

How do you approach building MLOps infrastructure for a client who is new to machine learning?

I start by understanding their business objectives and data maturity. Then I design a phased approach: first, establish a simple but scalable pipeline for training and deployment using a cloud ML platform (e.g., SageMaker, Vertex AI). Next, introduce automated testing and monitoring. I prioritize quick wins to demonstrate value while gradually building a robust MLOps foundation. I also advocate for clear ownership and governance from the start.

Can you give an example of a difficult technical challenge you solved in a previous ML project, and how you handled it?

In one project, we faced severe data drift after deploying a model, causing accuracy to drop. I implemented a monitoring system that tracked distribution changes in real-time. I then set up an automated retraining pipeline that triggered when drift exceeded thresholds. This required collaboration with the data engineering team to ensure streaming data sources were reliable. The solution improved model stability and reduced manual intervention.

How do you ensure that your solutions align with both the technical requirements and the business goals of a client?

I begin by conducting discovery workshops to understand the client's business context, success metrics, and constraints. I then map technical architecture decisions to business outcomes, such as cost efficiency, time-to-market, or prediction accuracy. I regularly communicate with stakeholders to ensure alignment and adjust priorities based on feedback. I also use agile delivery to iterate quickly based on business value.

Describe your experience working with cloud data platforms like Snowflake, AWS, or GCP in context of ML workflows.

I have extensive experience integrating ML workflows with cloud data platforms. For example, on AWS, I use SageMaker for modeling, Glue for ETL, and Redshift for data warehousing. With Snowflake, I leverage its ability to handle structured and semi-structured data, and I use Snowpark for feature engineering. I also implement ML pipelines that read from Snowflake for training data and write predictions back for analytics.

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.

6x Snowflake Partner of the Year (2020, 2021, 2022, 2023, 2024, 2025)
Fivetran, dbt, Atlation, and AWS Partner of the Year
#1 Partner in Snowflake Advanced Certifications
600+ Expert Cloud Certifications (Sigma, AWS, Azure, Dataiku, etc)

Recognized as an award-winning workplace in the US, India, and LATAM

We are looking for a Sr. Machine Learning Architect to join our Machine Learning team. In this role, you will lead the architecture and implementation of production-grade machine learning and data solutions that enable customers to realize tangible business value from their data. You will collaborate closely with clients, data scientists, data engineers, platform/DevOps teams, and practice leadership to deliver high-quality solutions and advance phData’s delivery excellence.

Key Responsibilities

Client Delivery

Own and drive end-to-end architecture, solution design, and delivery of machine learning and data solutions for enterprise clients across diverse industries.
Translate business and data science requirements into scalable technical and MLOps solutions that align with phData methodologies, standards, and best practices.
Ensure engagements are delivered on time, within scope, and with measurable business value for clients.
Design and create secure, scalable environments and tooling for data scientists to build, train, and manipulate models and data.
Work within customer technology ecosystems to extract data from a variety of source systems and place it within analytical and model-training environments.
Define deployment approaches and production infrastructure for machine learning models, ensuring that businesses can reliably use, monitor, and maintain the models we develop.
Demonstrate and reveal the business value of data by partnering with data scientists to manipulate and transform data into actionable insights and deployable machine learning models.
Create and execute operational testing strategies, including QA validation, performance testing, and implementation plans, to support model testing and deployment.
Ensure the quality, reliability, and observability of delivered solutions through testing, documentation, logging, and monitoring.

Collaboration & Leadership

Collaborate with cross-functional partners, including data science, data engineering, platform/DevOps, and business stakeholders, to deliver successful client engagements.
Provide technical and strategic leadership during workshops, discovery sessions, architecture and design reviews, and project delivery.
Ensure high quality in deliverables through code reviews, documentation, testing, governance, and adherence to security and compliance standards.
Partner with practice and account leaders to identify opportunities to expand engagements, improve delivery, and standardize patterns for deploying and operating ML solutions.
Serve as a technical thought leader for clients, recommending technologies and solution designs for model inference, retraining, monitoring, and lifecycle management from the application layer down to infrastructure.

Practice & Firm Contribution

Contribute to internal initiatives such as IP development, accelerators, reference architectures, templates, playbooks, and training related to machine learning engineering and MLOps.
Represent phData with professionalism in all interactions, communicating clearly with both technical and non-technical stakeholders.

Additional Responsibilities

Act as a trusted advisor to senior client stakeholders, shaping roadmaps, influencing strategic decisions, and guiding long-term initiatives.
Mentor and coach team members, fostering a culture of learning, feedback, and continuous improvement.
Help define and refine practice standards, reusable assets, and delivery frameworks.

About You

You are a technical leader and client-focused consultant who enjoys turning complex machine learning ideas into robust, production-ready solutions. You are comfortable working across data, infrastructure, and application layers, partnering directly with data scientists, engineers, and business stakeholders. You thrive in an outcomes-driven environment, navigating complex customer ecosystems to design architectures that are performant, secure, scalable, and maintainable.

Required Qualifications

Experience

6+ years of experience as a Machine Learning Engineer, Software Engineer, or Data Engineer building and deploying production data and machine learning solutions.

Technical / Functional Skills

Hands-on expertise in modern programming languages such as Python, Scala, Java, or similar, including experience developing APIs and web applications using frameworks such as Flask, Django, or Spring.
Experience building and operating robust data pipelines and distributed data processing solutions using SQL and big data technologies (e.g., Spark, Snowflake, Databricks, Redshift, Amazon EMR, HDFS).
Strong systems-level knowledge of network and cloud architecture, Linux-based operating systems, and data/storage platforms (e.g., AWS, Databricks, Cloudera), with familiarity across data and messaging systems such as JMS, Kafka, RDBMS, data warehouses, MySQL, Oracle, and SAP; proven experience deploying machine learning models in production environments.
Strong working knowledge of SQL and the ability to write, debug, and optimize complex and distributed queries.
Hands-on experience with one or more big data ecosystem products and languages such as Spark, Snowflake, Databricks, etc.
Production experience in core data technologies and platforms (e.g., Spark, HDFS, Snowflake, Databricks, Redshift, Amazon EMR).
Complete software development lifecycle experience, including design, documentation, implementation, testing, deployment, and ongoing operations.
Excellent communication and presentation skills, with previous experience working directly with internal or external customers.

Consulting / Delivery Skills

Participating in pre-sales or project scoping; as well as account growth / revenue generation with external clients
Experience delivering projects for external or internal clients in a professional services or consulting environment.
Ability to break down complex problems into structured, actionable steps and drive them through to completion.
Strong written and verbal communication skills in English.
Comfort presenting technical solutions to external clients and facilitating discussions with both technical and business stakeholders.

Collaboration & Ownership

Demonstrated ability to work effectively with distributed and cross-functional teams, including data scientists, engineers, and business stakeholders.
Proven track record of taking ownership, managing multiple priorities, and delivering high-quality work with minimal supervision.

Education

Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience preferred.

Preferred Qualifications

Preferred qualifications help candidates stand out but are not required for success in this role.

Experience in specific industry verticals or problem spaces where machine learning and data platforms are applied at scale (e.g., personalization, forecasting, risk modeling, operations optimization).
Hands-on experience with ecosystem technologies and cloud platforms such as Spark, Databricks, Snowflake, AWS, Azure, or GCP, and experience working with ML tooling such as AWS SageMaker, Azure ML, and MLflow, as well as libraries such as TensorFlow, Keras, scikit-learn, or H2O.
Prior experience working in global or remote teams and partnering across US, LATAM, and/or India.
Contributions to open source technology stacks, technical communities, speaking, or writing are a plus.
A Master’s or other advanced degree in data science, computer science, or a related field.

Location & Time Zone Expectations

This role is based in the United States and operates primarily in the Central Time Zone.

We are a remote-first company, and you should be comfortable working with a distributed global team.
Some flexibility may be required to collaborate across time zones with colleagues and clients.
Client needs may occasionally require flexibility in working hours to support key milestones or workshops.

Why phData?

Impactful Work: Partner with leading organizations on meaningful data & AI initiatives.
Collaborative Culture: Work with a supportive, high-performing global team that values transparency, autonomy, and continuous improvement.
Growth Opportunities: Access to challenging projects, mentorship, and structured development pathways.

Values-Driven: We prioritize doing the right thing for our clients, our teams, and our community.

Benefits at phData

LATAM:

Remote-First Work Environment
Casual, award-winning small-business work environment
Collaborative culture that prizes autonomy, creativity, and transparency
Competitive comp, excellent benefits, generous PTO plan 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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