Serve as a trusted advisor to stakeholders: understand their business goals, data challenges, ML/AI ambitions, and define technical strategies accordingly.
Architect end-to-end data & ML solutions on Databricks + AWS: from ingestion, storage, processing, feature engineering, to model training, serving, monitoring and operations.
Present and demonstrate solution architectures, proof-of-concepts, and reference implementations to technical & non-technical stakeholders.
Define and evolve the architecture for our unified data & ML platform: integrating Databricks and AWS services for compute, storage, orchestration, governance and security.
Lead hands-on implementation of platform components: infrastructure provisioning (IaC), DevSecOps pipelines, DataOps workflows, ML pipelines and operationalization.
Collaborate with internal teams to deliver robust, scalable, secure ML/AI platforms that support multiple markets/environments.
Work directly with engineering/data science teams to implement solutions, troubleshoot issues, tune performance and optimize cost.
Provide best-practice guidance, documentation, and enablement to stakeholders and internal peer teams (engineering, data science, ops).
Monitor solution adoption, drive continuous improvement, and act as the voice of the business stakeholders to internal product/engineering teams.
Stay ahead of emerging technologies in Databricks, AWS, ML/AI — and translate them into new solution patterns and accelerator frameworks Promote reusable reference architectures, automation templates, observability frameworks and governance guardrails that enable scale and repeatability.
Requirements
Deep technical expertise in **Databricks and AWS**, including integration of services across both platforms (e.g., Delta Lake, EMR, Glue, S3, SageMaker).
Proven hands-on implementation skills: capable of designing and delivering complex systems end to end — infrastructure, data, ML pipelines, monitoring and operations.
Experience building **scalable ML/AI platforms**: ingesting & processing large volumes of data, feature engineering, model training/deployment, monitoring and lifecycle management.
Strong programming experience with Python, SQL; familiarity with Spark; experience with IaC (Terraform, CloudFormation), orchestration (Airflow, Databricks Workflows) and container/Kubernetes environments.
Stakeholder-facing orientation: ability to engage with both technical and business stakeholders, translate requirements into solutions, and present ideas clearly.
Solid understanding of DevSecOps, DataOps, observability, cost-governance and platform reliability best-practices.
Excellent communication and collaboration skills; experience working across geographies and in fast-moving, multi-stakeholder environments.
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