Sr Data & AI Solution Architect

Responsibilities

• Data & AI Architecture Design and implement enterprise-scale data architectures supporting analytics, machine learning, and AI applications.
• Architect modern data platforms including data lakes, lakehouses, warehouses, and real-time pipelines.
• Define AI/ML solution architecture including model training, deployment, monitoring, and governance.
• Clouds & Platform Engineering Lead architecture for data and AI workloads on cloud platforms (AWS, Azure, or GCP).
• Implement scalable pipelines using technologies such as Spark, Databricks or similar.
• Optimize system performance, scalability, security, and cost.
• AI & Machine Learning Solutions Architect and guide development of AI-driven solutions including predictive analytics, NLP, recommendation systems, and generative AI applications.
• Support MLOps and AI lifecycle management including model deployment, monitoring, and versioning.
• Enterprise Integration Design integrations between data platforms, operational systems, and AI services.
• Establish APIs, event-driven architectures, and data sharing frameworks.
• Governance & Security Define and implement data governance, security, and compliance standards.
• Ensure adherence to privacy regulations and enterprise security frameworks.
• Stakeholder Engagement Collaborate with business leaders, product teams, and engineering teams to translate business problems into technical solutions.
• Lead architecture reviews and provide technical guidance across multiple initiatives.
• Led data engineers, ML engineers, and architects.
• Establish architecture standards, design patterns, and best practices.
• Influence strategic technology decisions across the organization.

Qualifications

• 10+ years of experience in data engineering, analytics, or platform architecture.
• 5+ years designing cloud-based data platforms and AI/ML solutions.
• Strong expertise with data architecture patterns (data lake, lakehouse, warehouse).
• Experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn, or similar).
• Hands-on experience with big data technologies (Spark, Databricks, Hadoop ecosystem, etc.).
• Strong knowledge of SQL, Python, and distributed data systems.
• Experience implementing MLOps pipelines and model lifecycle management.
• Experience with Generative AI and LLM-based applications.
• Knowledge of data governance platforms and metadata management.
• Experience with real-time data streaming platforms (Kafka, Kinesis, etc.).
• Industry experience in retail, e-commerce, finance, or supply chain.
• Relevant certifications (AWS/Azure/GCP Architect, Databricks, Snowflake).

Pay

Base Salary: $165,000 - $190,000 Annually
Annual Bonus Program
Equity
401(k) Profit Sharing
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