ML Solutions Architect

ML Solutions Architect

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

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 (Python, Java, Scala) to build and operate production environments while ensuring performance, security, scalability, and robust data integration.

• 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.

• Minimum of 6 years of experience as a Machine Learning Engineer, Software Engineer, or Data Engineer.
• Bachelor’s degree in Computer Science or a related technical field.
• Proven experience deploying machine learning models into live production environments.
• Expertise in Python, Scala, Java, or another modern programming language.
• Ability to build and operate robust data pipelines using a variety of data sources, programming languages, and toolsets.
• Strong working knowledge of SQL, including the ability to write, debug, and optimize distributed SQL queries.
• Hands‑on experience with technologies like Spark, Snowflake, or Databricks.
• Familiarity with multiple data sources and messaging systems (JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP).
• Systems‑level knowledge of network/cloud architecture, operating systems (Linux), and storage systems (AWS, Databricks, Cloudera).
• Production experience with enterprise data technologies (Spark, HDFS, Snowflake, Databricks, Redshift, Amazon EMR).
• Experience developing APIs and web server applications (Flask, Django, Spring).
• Full software development lifecycle experience, including design, documentation, implementation, testing, and deployment.
• Excellent communication and presentation skills, with previous experience interfacing with internal or external customers.

• Advanced Degree:
  Master’s or PhD in Data Science, Computer Science, or a related technical field.
• Hands‑on experience with major cloud provider ecosystems (AWS, Azure, GCP) and advanced data platforms.
• Experience working with data science and machine learning libraries such as h2o, Tensor Flow, Keras, or scikit‑learn.
• Experience with AWS Sage Maker, Azure ML, or MLflow.
• Familiarity with Docker, Kubernetes, or equivalent container technologies.
• Prior experience building and scaling enterprise‑grade machine learning models.
• Relevant side projects or contributions to open‑source technology stacks.

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 life cycles.

• Architect and deploy production‑scale machine learning systems using an enterprise‑grade cloud stack.
• Drive technological selection and solution design from the application layer to core infrastructure.
• Collaborate with senior data scientists, software engineers, and domain experts to deliver high‑value systems.
• Evaluate, adopt, and integrate emerging big data and MLOps technologies.