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.
Provectus