ML Solutions Architect

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, Google Cloud Platform) 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.