The Machine Learning Solution Architect will design, plan, and implement scalable data and machine learning systems across cloud and on-premise platforms. This role involves leading technical customer engagements, delivering innovative AI and ML solutions, and serving as a trusted advisor to stakeholders by aligning technology with business objectives and best practices.
Responsibilities
- Design and deploy data and AI/ML architecture solutions across cloud and on-premise environments.
- Lead complex customer engagements by providing strategic technical direction and aligning solutions with business objectives.
- Develop and maintain trusted relationships with key customer stakeholders through technical advisory services.
- Conduct technical workshops, training sessions, and presentations for diverse audiences.
- Define and manage end-to-end data lifecycle processes including ingestion, storage, processing, and visualization.
- Work with business units and stakeholders to ensure solutions support business goals.
- Ensure all solutions comply with security, compliance, and architectural standards such as AWS Well-Architected and GCP Architecture Framework.
- Lead and mentor cross-functional technical teams, providing guidance and expertise.
- Design and execute proofs of concept for emerging technologies including Generative AI and machine learning.
- Promote best practices in backend and ML services to ensure scalable and maintainable systems.
- Oversee data governance and data quality initiatives across platforms.
- Stay current with evolving technology trends and continuously refine architectural strategies.
Requirements
- Minimum of 7 years in solutions architecture with a focus on Big Data and cloud platforms such as AWS, GCP, or Azure.
- Strong communication and problem-solving abilities, with experience presenting technical concepts to both technical and non-technical audiences across multiple projects.
- Proven experience in technical sales or pre-sales for cloud, big data, and machine learning solutions.
- Demonstrated leadership and collaboration skills in team environments.
- Strategic mindset with a focus on delivering measurable business outcomes.
- Track record of building strong client relationships and serving as a trusted technical advisor.
- Expertise in data engineering and analytics, including designing data pipelines and architectures using AWS, GCP, or Azure data platforms.
- Solid understanding of AI and ML concepts, with hands-on experience integrating ML components into solutions.
- Demonstrated experience working with data lakes, data warehouses, and real-time data analytics systems.
- Experience designing solutions using microservice architecture and containerized deployment models.
- Hands-on experience with Kubernetes, Docker, and managing containerized applications.
- Proficiency in backend programming languages such as TypeScript, Java, or Python.
- In-depth knowledge of machine learning and MLOps tools including PyTorch, SageMaker, and MLFlow.
- Proven ability to lead and mentor cross-functional technical teams.
- Familiarity with agile development methodologies.
Nice to Have
- Experience implementing Generative AI solutions.
- Proficiency with graph databases such as Neo4j or AWS Neptune.
- Understanding of data mesh principles and data contracts.
- Operational experience with infrastructure as code tools like AWS CDK, CloudFormation, or Terraform.
Tech Stack
AWS, GCP, Azure, Kubernetes, Docker, PyTorch, SageMaker, MLFlow, Neo4j, AWS Neptune, AWS CDK, CloudFormation, Terraform, TypeScript, Java, Python
Work Arrangement
global — North America, LATAM, EMEA — Position supports global collaboration with offices across multiple regions.
Team
Cross-functional teams including data engineers, ML specialists, and client stakeholders.
- Focused on using cloud, data, and AI to transform how clients operate and compete.
- Dedicated to building ML infrastructure that enables end-to-end AI transformation.
- Committed to helping organizations adopt and scale AI use cases across the enterprise.
Additional Information
- Industries served include Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses.
- Role requires serving as a trusted technical advisor to clients.
- Technical sales or pre-sales experience is a requirement.
- Must be able to lead technical workshops and presentations for both technical and non-technical audiences.
- Familiarity with agile methodologies is expected.
