ML Solutions Architect

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.