Architect – AI & Data

Job Details

  • Salary
  • Experience 10 Years Experience

Architect – AI & Data

  • Sri Lanka

Role Summary

As an Architect – Data & AI, you will be responsible for designing and owning enterprise-grade data and AI solution architectures across modern cloud and hybrid environments. This role combines deep expertise in data engineering, machine learning, and intelligent automation to deliver scalable, secure, and high-performing platforms.

You will define end-to-end architectures spanning data ingestion, transformation, storage, orchestration, and AI/ML model deployment. Working closely with both pre-sales and delivery teams, you will translate complex business requirements into robust technical solutions and guide engineering teams through successful implementation.

Key Responsibilities

  • Design end-to-end data platform architectures supporting batch and real-time ingestion, ETL/ELT processing, cloud storage, orchestration, and data serving across AWS, Azure, and GCP.
  • Architect scalable cloud data platforms including data lakes, lakehouses (Delta Lake, Apache Iceberg), and data warehouses (Snowflake, BigQuery, Azure Synapse, Amazon Redshift).
  • Develop real-time and event-driven data pipelines using Kafka, Kinesis, Event Hubs, and orchestration tools such as Airflow, LangGraph, and n8n.
  • Define AI/ML solution architectures across use cases such as predictive analytics, NLP, computer vision, and generative AI, ensuring seamless integration into data platforms.
  • Design and implement MLOps frameworks including experiment tracking, model registry, automated retraining, and monitoring using MLflow, Kubeflow, SageMaker Pipelines, or Azure ML.
  • Establish and enforce data governance frameworks covering data quality, lineage, cataloguing, and access control using tools such as Microsoft Purview, AWS Glue Data Catalog, and Apache Atlas.
  • Lead technical discovery sessions and workshops with stakeholders, translating business needs into scalable solution architectures and implementation roadmaps.
  • Support pre-sales initiatives by contributing to RFPs, solution proposals, and client presentations.
  • Provide technical leadership by reviewing engineering designs and ensuring adherence to architectural standards, security, and governance best practices.
  • Create and maintain architecture documentation including solution designs, data flow diagrams, and technical decision records.

Key Performance Indicators (KPIs)

  • Deliver a minimum of three end-to-end Data & AI solutions per cycle aligned with business and quality standards.
  • Contribute to improved pre-sales win rates through strong technical solutioning and positioning.
  • Build and maintain reusable architecture patterns and reference frameworks to accelerate delivery.
  • Ensure zero critical architectural rework through thorough design validation and governance.
  • Achieve high stakeholder satisfaction in terms of solution effectiveness, scalability, and reliability.

Ideal Candidate Profile

  • Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field.
  • 6–9 years of experience in data engineering and AI/ML, including 2–3 years in an architect or principal-level role.
  • Strong expertise in designing enterprise-scale cloud data platforms across AWS, Azure, and/or GCP.
  • Hands-on experience with cloud-native data and AI services such as AWS Glue, Redshift, SageMaker; Azure Data Factory, Synapse, Azure ML; BigQuery and Vertex AI.
  • Proven experience in building ETL/ELT pipelines using Apache Spark and dbt, with strong SQL and data modelling skills (star schema, Data Vault 2.0, OBT).
  • Experience with AI/ML frameworks including TensorFlow, PyTorch, scikit-learn, and Hugging Face.
  • Strong knowledge of MLOps tools such as MLflow, Kubeflow, and SageMaker Pipelines.
  • Familiarity with lakehouse architectures (Delta Lake, Apache Iceberg) and workflow orchestration tools (Airflow, Prefect, Dagster).
  • Experience with data governance tools such as Microsoft Purview, Collibra, or Apache Atlas.
  • Hands-on experience with containerization (Docker, Kubernetes) and infrastructure-as-code tools (Terraform, CloudFormation).
  • Strong communication skills with experience in stakeholder management, RFP contributions, and technical documentation.

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