Data/ ML Solution Architect (GenAI, AWS)

Responsibilities:

• Lead the design and implementation of data and AI/ML architecture solutions across cloud and on-premise platforms.
• Lead complex customer engagements, providing strategic technical vision and aligning solutions with customer business goals.
• Build and maintain strong relationships with key customer stakeholders, acting as a trusted technical advisor.
• Lead technical workshops, training sessions, and presentations.
• Define and execute data lifecycle processes: ingestion, storage, processing, and visualization.
• Collaborate with business units and stakeholders to align solutions with business goals.
• Ensure solutions adhere to security, compliance, and architecture frameworks (e.g., AWS Well-Architected, GCP Architecture Framework).
• Lead cross-functional teams, providing mentorship and guidance to technical talent.
• Design and execute proofs of concept for emerging technologies like Generative AI, Machine Learning
• Drive backend/ML services best practices for scalable and maintainable solutions.
• Oversee data governance and data quality processes across platforms.
• Stay updated with the latest technology trends and continuously improve the architecture strategy.

Requirements:

• 7+ years of experience in solutions architecture, with a strong focus on Big Data and cloud platforms (AWS, GCP, Azure).
• Excellent communication and problem-solving skills, with the ability to work across multiple projects and the ability to articulate complex technical concepts to both technical and non-technical audiences.
• Technical sales or pre-sales experience with cloud and big data, and ML solutions.
• Strong leadership and team collaboration abilities.
• Strategic thinking with a focus on delivering measurable business value.
• Proven ability to build strong relationships with customers and act as a trusted advisor.
• Proficiency in data engineering and analytics, designing data pipelines and architectures using AWS, GCP, or Azure data stack.
• Strong understanding of AI/ML concepts and experience integrating AI/ML components into solutions.
• Proven experience with data lakes, data warehouses, and real-time data analytics.
• Proven experience with microservice architecture and containerized deployment options.
• Hands-on experience with Kubernetes, Docker, and containerized applications.
• Proficiency in any of backend backend-related languages: TS, Java, Python, and others.
• Solid understanding of machine learning and MLOps tools (PyTorch, SageMaker, MLFlow).
• Demonstrated ability to lead and mentor cross-functional teams.
• Familiarity with agile methodologies.
 
Nice to Have:
• Experience in Generative AI implementations.
• Proficiency with graph databases (Neo4j, AWS Neptune).
• Knowledge of data mesh principles and data contracts.
• Operational knowledge of infrastructure deployment tools like AWS CDK, CloudFormation, and Terraform.