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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Solutions Architect - **Company:** LEOFORCE, LLC - **Location:** Chicago, IL, United States - **Experience:** Expert - **Salary:** $160,000.0 - $210,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Amazon Web Services, Computing Platforms, Microsoft Azure, Big Data, Business Systems, Cloud Computing, Cloud Engineering, Computer Programming, Data Warehousing, Relational Databases, Software Debugging, Linux, Django Web Framework, Hadoop Distributed File System, Web Servers, Java Message Service (JMS), Spring Framework, Python (Programming Language), Machine Learning, Enterprise Messaging Systems, MySQL, Oracle (Applications), Tensorflow, Standard Sql, Cloudera, Azure Machine Learning, SAP (Applications), Scala (Programming Language), Software Engineering, SQL Databases, Google Cloud, Flask (Web Framework), Snowflake, Apache Spark, Keras, Containerization, Scikit Learn, Kubernetes, Information Technology, Apache Kafka, Machine Learning Operations, Api Design, Data Pipelines, Amazon Elastic Mapreduce (EMR), Docker, Amazon Redshift, Databricks, Programming Languages - **Published:** July 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=1e35423a2c893529 ## About the Role 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. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [MySQL Protocol Features You Should Be Aware Of](https://www.wearedevelopers.com/videos/100267-mysql-protocol-features-you-should-be-aware-of) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers)