ML Solutions Architect

LeoForce; Chicago (IL); On-site; USD 160,000 - 210,000; Full time

Job summary

LeoForce is seeking a Senior ML Solutions Architect in Chicago to design and implement data solutions across the ML lifecycle, from model inference to monitoring. You will lead the development of scalable, secure environments, partner with data scientists, and guide tech choices from application to infrastructure layers.

You will leverage Python, Scala, and Java to build production-grade pipelines, optimize performance, and ensure robust data integration within an enterprise-grade cloud stack.

Qualifications

• Minimum of 6 years of experience as a Machine Learning Engineer, Software Engineer, or Data Engineer.
• Bachelor's degree in Computer Science or related field.
• Proven experience deploying machine learning models into live production environments.
• Expertise in Python, Scala, Java, or similar languages.
• Ability to build and operate robust data pipelines using diverse data sources.
• Strong SQL skills for distributed queries.
• Hands-on experience with Spark, Snowflake, or Databricks.
• Familiarity with data sources and messaging systems (e.g., Kafka, JMS, RDBMS).
• Knowledge of cloud architectures, Linux, and enterprise storage (AWS, Databricks).
• Experience with API development (Flask, Django, Spring).
• End-to-end SDLC knowledge; strong communication with customers.

Responsibilities

• Environment Creation: design and build data scientist environments for modeling.
• System Integration: securely extract and integrate data into analytical platforms.
• Deployment & Infrastructure: define deployment approaches and infra for models.
• Value Demonstration: translate raw data into actionable business insights.
• Lifecycle Management: ensure scalable, maintainable solutions across lifecycles.
• Testing & QA: develop testing strategies and oversee deployment.
• Quality Assurance: own overall quality, performance, and security of the product.

Skills

Python

Scala

Java

SQL

Communication

Leadership

Education

Bachelor's degree in Computer Science

Master's or PhD in Data Science/CS

Tools

Spark

Snowflake

Databricks

Redshift

AWS

Docker

Kubernetes

Flask

Django

Spring

Job description

ML Solutions Architect

Chicago,IL, US

Job Description

Experience: Senior Level Salary: $160,000 - $210,000 per year

Job Details

Position Overview As a Solutions Architect on our Machine Learning Engineering team, you will design and implement data solutions tailored to our customers' needs. Your scope will span the entire machine learning lifecycle, including model inference, retraining, monitoring, and beyond, across an evolving technical stack. In this role, you will provide thought leadership by recommending technologies and solution designs from the application layer to the infrastructure layer. You will leverage your team leadership and coding skills (e.g., Python, Java, Scala) to build and operate production environments while ensuring performance, security, scalability, and robust data integration.

Key Responsibilities

Environment Creation: Design and build environments for data scientists to manipulate data and construct machine learning models.

System Integration: Analyze customer technology environments to extract data securely and integrate it into analytical platforms.

Deployment & Infrastructure: Define deployment approaches and infrastructure for models, ensuring businesses can seamlessly utilize developed models.

Value Demonstration: Partner with data scientists to transform raw data into appropriate formats, unlocking actionable business insights through scalable machine learning models.

Lifecycle Management: Collaborate with data science teams to ensure solutions are deployable at scale, compatible with existing business systems, and maintainable throughout their lifecycle.

Testing & QA: Create operational testing strategies, validate models in QA environments, and oversee final implementation and deployment.

Quality Assurance: Take ownership of the overall quality, performance, and security of the delivered product.

Basic Qualifications

Experience: Minimum of 6 years of experience as a Machine Learning Engineer, Software Engineer, or Data Engineer.

Education: Bachelor's degree in Computer Science, or a related technical field.

Model Deployment: Proven experience deploying machine learning models into live production environments.

Programming: Expertise in Python, Scala, Java, or another modern programming language.

Data Pipelines: Ability to build and operate robust data pipelines using a variety of data sources, programming languages, and toolsets.

SQL Mastery: Strong working knowledge of SQL, including the ability to write, debug, and optimize distributed SQL queries.

Big Data Ecosystems: Hands-on experience with technologies like Spark, Snowflake, or Databricks.

Data Sources: Familiarity with multiple data sources and messaging systems (e.g., JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP).

Systems & Cloud: Systems-level knowledge of network/cloud architecture, operating systems (e.g., Linux), and storage systems (e.g., AWS, Databricks, Cloudera).

Core Data Tech: Production experience with enterprise data technologies (e.g., Spark, HDFS, Snowflake, Databricks, Redshift, Amazon EMR).

API Development: Experience developing APIs and web server applications (e.g., Flask, Django, Spring).

SDLC Knowledge: Full software development lifecycle experience, including design, documentation, implementation, testing, and deployment.

Communication: Excellent communication and presentation skills, with previous experience interfacing with internal or external customers.

Preferred Qualifications

Advanced Degree: Master's or PhD in Data Science, Computer Science, or a related technical field.

Cloud & Platform Expertise: Hands-on experience with major cloud provider ecosystems (AWS, Azure, GCP) and advanced data platforms.

ML Libraries: Experience working with data science and machine learning libraries such as h2o, TensorFlow, Keras, or scikit-learn.

MLOps Tools: Experience with AWS SageMaker, Azure ML, or MLflow.

Containerization: Familiarity with Docker, Kubernetes, or equivalent container technologies.

Enterprise ML: Prior experience building and scaling enterprise-grade machine learning models.

Community Engagement: Relevant side projects or contributions to open-source technology stacks.

A bit about us:

We are a data-driven technology company dedicated to delivering robust infrastructure and scalable analytics solutions for our clients. Our engineering teams design, build, and maintain enterprise-grade environments that turn complex data into measurable business outcomes. We place a high priority on technical excellence, system security, and continuous innovation across our software and machine learning lifecycles.

Why join us?

Complex Technical Challenges: Architect and deploy production-scale machine learning systems using an enterprise-grade cloud stack.

Professional Autonomy: Drive technological selection and solution design from the application layer to core infrastructure.

Collaborative Environment: Work alongside senior data scientists, software engineers, and domain experts to deliver high-value systems.

Continuous Innovation: Evaluate, adopt, and integrate emerging big data and MLOps technologies.

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