ML Solutions Architect
$160k - $210kJobot
Remote Position This Jobot Job is hosted by: Jim Quirk
Are you a fit? Easy Apply now by clicking the "Apply" button and sending us your resume.
Salary: $160,000 - $210,000 per year 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. 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. Interested in hearing more? Easy Apply now by clicking the "Apply" button. Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot’s policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions. Sometimes Jobot is required to perform background checks with your authorization. Jobot will consider qualified candidates with criminal histories in a manner consistent with any applicable federal, state, or local law regarding criminal backgrounds, including but not limited to the Los Angeles Fair Chance Initiative for Hiring and the San Francisco Fair Chance Ordinance. Information collected and processed as part of your Jobot candidate profile, and any job applications, resumes, or other information you choose to submit is subject to Jobot's Privacy Policy, as well as the Jobot California Worker Privacy Notice and Jobot Notice Regarding Automated Employment Decision Tools which are available at jobot.com/legal. By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails from Jobot, and/or its agents and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel and HELP for help. You can access our privacy policy here: jobot.com/privacy-policy
Are you a fit? Easy Apply now by clicking the "Apply" button and sending us your resume.
Salary: $160,000 - $210,000 per year 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. 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. Interested in hearing more? Easy Apply now by clicking the "Apply" button. Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot’s policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions. Sometimes Jobot is required to perform background checks with your authorization. Jobot will consider qualified candidates with criminal histories in a manner consistent with any applicable federal, state, or local law regarding criminal backgrounds, including but not limited to the Los Angeles Fair Chance Initiative for Hiring and the San Francisco Fair Chance Ordinance. Information collected and processed as part of your Jobot candidate profile, and any job applications, resumes, or other information you choose to submit is subject to Jobot's Privacy Policy, as well as the Jobot California Worker Privacy Notice and Jobot Notice Regarding Automated Employment Decision Tools which are available at jobot.com/legal. By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails from Jobot, and/or its agents and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel and HELP for help. You can access our privacy policy here: jobot.com/privacy-policy
Vacancy posted 10 hours ago
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