Data/ML Solutions Architect (AWS)

Responsibilities:

• Strategic Technical Leadership:
- Lead high-impact customer engagements focused on AWS Data Platform/ ML solutions.
- Define and drive technical strategies that align AWS capabilities with customer objectives, incorporating Databricks, GCP, or Azure where appropriate.
• Solution Architecture and Design:
- Lead the design and implementation of data and AI/ML architecture solutions across cloud and on-premise platforms, ensuring optimal performance, security, and cost-efficiency..     
- Design and execute proofs of concept for emerging technologies like Generative AI, Machine Learning
- Integrate AWS services with other solutions (Databricks, Snowflake, GCP, or Azure) as needed, selecting the right technologies and tools to meet customer needs.
- Develop and maintain comprehensive architectural documentation aligned with organizational technical standards.
• Pre-Sales Activities:
- Partner with the sales team, providing technical expertise to position AWS-based data solutions effectively.
- Participate in customer meetings to assess technical needs, scope solutions, and identify growth opportunities.
- Create technical proposals, solution architectures, and presentations to support sales efforts and align with customer expectations.
- Assist in responding to RFPs/RFIs with accurate technical input and align solutions to client requirements.
- Demonstrate AWS capabilities through POCs and technical demonstrations to showcase proposed solutions.
• Customer Engagement and Relationship Management:
- Build and maintain strong relationships with key customer stakeholders, acting as a trusted advisor for data platform initiatives.
- Lead discovery workshops to understand customer requirements, KPIs, and technical constraints.
• Project Leadership and Delivery:
- Oversee the end-to-end implementation of AWS-based data platforms, coordinating with engineering teams to ensure successful delivery.
- Manage technical risks and develop mitigation strategies.
• Innovation and Best Practices:
- Stay up-to-date with the latest developments in AWS, Databricks, GCP, Azure, and cloud technologies.
- Develop and promote best practices in data platform architecture, data pipelines, and data governance.
• Cross-Functional Collaboration:
- Collaborate with AI/ML teams to integrate advanced analytics and machine learning capabilities into AWS and other cloud platforms.
- Work with DevOps teams to implement CI/CD pipelines and automation for data workflows.
• Mentorship and Knowledge Sharing:
- Mentor junior architects and engineers, fostering a culture of continuous learning and professional development.
- Contribute to knowledge-sharing initiatives through technical blogs, case studies, and industry event presentations.
• Governance, Compliance, and Security:
- Ensure that AWS-based data platform solutions comply with relevant security standards and regulations.
- Implement data governance frameworks to maintain data quality and integrity.

Requirements:

• Experience in data solution architecture.
• Strong understanding of AI/ML concepts and experience integrating AI/ML components into solutions.
• Technical sales or pre-sales experience with cloud, Big Data, and ML solutions.
• Proven experience in designing and implementing large-scale data engineering solutions on AWS.
• Experience with Databricks, GCP, or Azure solutions is required.
• Deep expertise in AWS platform services, including S3, EC2, Lambda, EMR, Glue, Redshift, AWS MSK, and EKS.
• Proficiency in any of the backend-related languages: TS, Java, Python, Scala, and others.
• Experience with data warehousing, ETL processes, and real-time data streaming.
• Familiarity with open-source technologies and tools in data engineering.
• Solid understanding of machine learning and MLOps tools (PyTorch, SageMaker, MLFlow).
• Hands-on experience with Kubernetes, Docker, and containerized applications.
• AWS Certified Solutions Architect – Professional (or similar) is required.
• Excellent communication and presentation skills, with the ability to convey complex technical concepts to non-technical stakeholders.
• Strong leadership and project management skills.
• Ability to work collaboratively in a cross-functional team environment.

Will Be a Plus:

• Experience in the Healthcare and Biotech domains.
• Certifications in Databricks, GCP, or Azure.
• Experience with AWS Migration Acceleration Programs (MAP).
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Contributions to open-source projects or active participation in the data engineering community.