Principal Data Solutions Architect- AI/ML

Position: Data & Analytics Solutions Architect- AI/ML

Location: Toronto, ON (Hybrid)

Employment Type: Full-Time

Experience Required: 14 + Years

Position Overview:

We are seeking an experienced Data & Analytics Solutions Architect to lead the design and implementation of modern enterprise data platforms, advanced analytics solutions, and AI-driven architectures. The ideal candidate will have extensive experience in architecting large-scale open-source data ecosystems, distributed data processing platforms, Data Lakehouse architectures, and AI/ML-enabled analytics solutions.

Key Responsibilities:

• Engage with business and technology stakeholders to understand data strategy, platform requirements, architecture goals, and transformation priorities.
• Design and define end-to-end architecture for enterprise open-source data platforms supporting batch, streaming, analytics, and AI workloads.
• Lead modernization initiatives covering data lakes, lakehouse platforms, distributed processing frameworks, and enterprise data ecosystems.
• Architect scalable, secure, highly available, and resilient data platforms using open-source technologies and industry best practices.
• Design data ingestion, integration, streaming, storage, and consumption patterns using technologies such as Spark, Kafka, Flink, NiFi, and Trino.
• Establish data governance, security, metadata, lineage, privacy, and observability standards across the platform landscape.
• Evaluate and recommend open-source technologies based on business requirements, scalability, supportability, interoperability, and total cost of ownership.
• Define architecture standards, reference architectures, reusable frameworks, CI/CD practices, and DataOps operating models.
• Provide technical leadership across data modeling, data engineering, platform implementation, migration, testing, deployment, and production readiness activities.
• Drive platform performance optimization, capacity planning, reliability engineering, and operational excellence initiatives.
• Conduct architecture reviews, identify risks early, and guide delivery teams on mitigation strategies and best practices.
• Support solutioning, RFP responses, effort estimation, technical presentations, and client workshops for strategic opportunities.
• Collaborate with engineering teams, platform vendors, open-source communities, and client stakeholders to deliver scalable and sustainable solutions.
• Mentor architects and engineers, contribute to technical capability development, and establish reusable accelerators, playbooks, and best practices.
• Support hiring, technical assessments, and knowledge-sharing initiatives across Data Engineering, Open-Source Platforms, and Modern Data Architecture.
• Assess candidate use cases with business, technology, data, and risk stakeholders; recommend BI, rules, analytics, ML, or GenAI based on feasibility, risk, cost, and measurable business outcomes.
• Integrate data, ML, and GenAI reference architectures covering data sources, feature pipelines, models or LLMs, vector stores, serving layers, consuming applications, and enterprise integration points.
• Partner with platform and operations teams to productionize AI ML and LLM workloads on Kubernetes.

Required Skills:

• 14+ years of experience in Enterprise Data Architecture, Open-Source Data Platforms, Distributed Data Processing, and Data Engineering.
• Proven experience in Enterprise Solution architecture using Open source technology stack across the Data to Insights end to end value chain.
• Strong hands-on expertise in Apache Spark, PySpark, Kafka, Flink, Airflow, NiFi, Trino, SQL, Python, Scala, and Java.
• Proven experience in designing and implementing open-source data platform architectures, including component selection, integration, scalability, interoperability, and platform modernization.
• Deep understanding of batch, streaming, and real-time data processing architectures, including performance tuning, workload optimization, and failure recovery.
• Experience building modern Data Lakehouse architectures using Apache Iceberg, Hudi, Delta Lake, object storage, and open table formats.
• Strong knowledge of event-driven architectures, CDC, APIs, schema management, and enterprise integration patterns.
• Expertise in DataOps practices, including Airflow orchestration, CI/CD, automated testing, observability, metadata management, lineage, and platform operations.
• Experience with PostgreSQL, Cassandra, MongoDB, OpenSearch, and enterprise analytics platforms, including data modeling, indexing, workload management, and availability design.
• Strong understanding of security, governance, and compliance, including RBAC, identity management, encryption, privacy controls, auditability, metadata governance, and vulnerability management.
• Proven ability to lead architecture definition, HLD/LLD creation, NFR assessment, migration planning, technical governance, and production readiness activities.
• Experience supporting large-scale data platform modernization, cloud migration, solutioning, RFP responses, effort estimation, and client-facing architecture discussions.
• Good understanding of BFSI regulatory, security, resilience, privacy, and operational requirements.
• Bachelor’s degree in computer science, Engineering, Information Systems, Data Management, or a related discipline.
• Preferred certifications in Cloud Data Engineering, Kubernetes, Enterprise Architecture, Data Management, Security, DevOps, or Open-Source Data Platforms.
• Experience assessing business use cases and selecting the appropriate approach across BI/KPI reporting, rules, statistical methods, forecasting, machine learning, and GenAI, with clear baselines, acceptance criteria, and measurable business KPIs.
• Understanding of AI/ML platform architecture supporting feature engineering, model training, batch and real-time inference, and integration of ML and GenAI workloads with enterprise open-source data platforms.
• Understanding of MLOps and LLMOps capabilities, including experiment tracking, model registry, feature stores, pipeline orchestration, automated quality gates, and versioning of data, models, and prompts using tools such as MLflow and Kubeflow.
• Exposure to AI observability and monitoring for data drift, model drift, LLM quality, tracing, latency, availability, alerting, and operational runbooks.