Designs and implements data and machine learning systems across cloud and on-premise platforms. Leads technical initiatives, provides strategic direction, and ensures AI/ML solutions support business objectives. Acts as a technical leader and mentor within a global AI-driven organization.
Responsibilities
- Architect and deploy data and AI/ML solutions across hybrid environments.
- Lead customer-facing technical engagements, delivering strategic alignment with business outcomes.
- Serve as a trusted advisor to key stakeholders through strong relationship-building.
- Conduct technical workshops, training, and solution demonstrations.
- Define and manage end-to-end data workflows including ingestion, storage, processing, and visualization.
- Collaborate with business teams to ensure technical solutions support organizational goals.
- Ensure compliance with security, governance, and cloud architecture standards.
- Mentor and guide technical teams across projects and regions.
- Design and execute proofs of concept for emerging technologies, including Generative AI and ML platforms.
- Promote best practices in backend and machine learning service design for scalability and maintainability.
- Oversee data governance, quality, and lifecycle management across systems.
- Stay current with evolving technologies to continuously refine architectural strategies.
Requirements
- Minimum of 7 years in solutions architecture with emphasis on Big Data and cloud platforms.
- Strong communication and analytical skills for diverse technical and non-technical audiences.
- Experience in pre-sales or technical sales for cloud, big data, or machine learning solutions.
- Proven leadership and collaboration skills in cross-functional environments.
- Strategic mindset focused on delivering measurable business impact.
- Demonstrated ability to build trust and long-term relationships with clients.
- Expertise in designing data pipelines and architectures using AWS, GCP, or Azure.
- Solid understanding of AI/ML concepts and integration into production systems.
- Hands-on experience with data lakes, warehouses, and real-time analytics platforms.
- Familiarity with microservices and container-based deployment models.
- Practical knowledge of Kubernetes and Docker for application orchestration.
- Proficiency in backend programming languages such as TypeScript, Java, or Python.
- Experience with MLOps tools including PyTorch, SageMaker, and MLFlow.
- Track record mentoring technical teams and leading complex implementations.
- Working knowledge of agile development practices.
Nice to Have
- Hands-on experience deploying Generative AI solutions.
- Familiarity with graph databases such as Neo4j or AWS Neptune.
- Understanding of data mesh concepts and data contract frameworks.
- Operational experience with infrastructure-as-code tools like AWS CDK, CloudFormation, or Terraform.
Tech Stack
AWS, GCP, Azure, Kubernetes, Docker, PyTorch, SageMaker, MLFlow, AWS CDK, CloudFormation, Terraform, Neo4j, AWS Neptune, TypeScript, Java, Python
Work Arrangement
Global remote work with team presence across North America, LATAM, and EMEA regions
Team
Cross-functional teams focused on AI/ML and data architecture projects
- Driven by cloud, data, and AI to transform client operations
- Focused on innovation and full lifecycle AI transformation
- Built on collaborative partnerships with global clients
- Committed to delivering measurable business outcomes through technology
Additional Information
- Serves industries including Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses
- Specializes in building ML infrastructure to enable end-to-end AI transformation
- Supports organizations in adopting and scaling AI use cases enterprise-wide
