We are seeking a seasoned Solutions Architect who brings deep experience in architecting enterprise-grade platformsalong with a strong track record in data products and AI solutions. This role requires a strategic thinker who can bridge business goals with scalable technology architectures—leading end-to-end solution design across cloud, data, and AI ecosystems.

You will work closely with business stakeholders, data scientists, product owners, and engineering teams to conceptualize and deliver innovative products that power modern enterprises.

Key Responsibilities

• Architect scalable and secure solutions for enterprise applications, data platforms, and AI-driven products.
• Lead technical design sessions, ensuring alignment with business goals and platform vision.
• Define end-to-end architectures across cloud (preferably Azure), data pipelines, APIs, and AI/ML models.
• Collaborate with cross-functional teams to translate requirements into architectural blueprints and technical specifications.
• Provide technical leadership throughout the product lifecycle—from discovery and PoC to deployment and optimization.
• Evaluate and recommend data storage, processing, and ML inference technologies suited to the product needs.
• Oversee solution governance, security, performance, and scalability standards.
• Support pre-sales or client discussions as the technical authority on product architecture when needed.

Required Qualifications

• Bachelor's or Master’s in Computer Science, Software Engineering, or a related field.
• 8+ years of experience in software architecture and solution design.
• Proven experience in architecting cloud-native applications and enterprise platforms.
• At least 3 years of experience in designing data products or AI-driven applications (ML models, data lakes, model orchestration, MLOps).
• Proficient in designing microservices, APIs, and distributed systems.
• Strong knowledge of data architectures (data lake, data mesh, lakehouse) and AI product workflows.
• Hands-on experience with Azure, AWS, or GCP, with a preference for Azure.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, MLflow) and modern data engineering stacks (Spark, Databricks, Airflow).