Platform (Cloud, Data & AI) Practice Lead

Platform (Cloud, Data & AI) Practice Lead

Organization:

Location:

Reports to:

Role Summary

As the Platform (Cloud, Data & AI) Practice Lead, you will architect, deliver, and scale enterprise-grade cloud, data engineering, and AI/ML platforms. You will define and implement frameworks, reusable assets, and best practices that enable teams to deliver secure, scalable, and innovative solutions across AWS, Azure, GCP, Snowflake, Databricks, and leading AI/ML platforms. Your leadership will ensure seamless integration of cloud infrastructure, modern data engineering, and advanced analytics to accelerate business value.

Key Responsibilities

• Strategic Leadership:

  Define and execute the integrated platform strategy and roadmap for cloud, data, and AI, aligned with business and technology goals.

• Cloud Platform Engineering:

  Architect and oversee the deployment of secure, scalable, and cost-optimized cloud platforms (AWS, Azure, GCP), including IaaS, PaaS, container orchestration (Kubernetes, OpenShift), and automation (Terraform, ARM, CloudFormation).

• Data Engineering:

  Lead the design and implementation of cloud-native data platforms, including data lakes, lakehouses, streaming, and batch pipelines using tools like Databricks, Snowflake, Apache Spark, Kafka, Airflow, DBT, and cloud-native services.

• AI/ML Platform Enablement:

  Build and operationalize AI/ML platforms (Azure ML, AWS SageMaker, GCP Vertex AI, Databricks ML), supporting model development, deployment, monitoring, and MLOps.

• Reusable Assets & IP:

  Develop and maintain reusable tools, accelerators, frameworks, and IP for platform engineering, data migration, AI/ML integration, and automation.

• AI-Embedded Solutions:

  Embed AI/ML capabilities into platform offerings, enabling advanced analytics, intelligent automation, and data-driven decision-making.

• DataOps & MLOps:

  Champion DataOps and MLOps practices (CI/CD, automated testing, monitoring, observability, lineage) for data and AI workloads.

• Monitoring & Observability:

  Implement and manage platform observability using Prometheus, Grafana, ELK/EFK Stack, Datadog, CloudWatch, Azure Monitor, Google Cloud Operations Suite, OpenTelemetry, Great Expectations, Monte Carlo, and OpenLineage.

• Executive & Customer Engagement:

  Present technical solutions and transformation roadmaps to executives and customers; deliver technical briefings, workshops, and thought leadership.

• RFPs, Proposals & SOWs:

  Actively participate in RFP responses, proposal development, and SOW creation for platform transformation opportunities.

• Collaboration:

  Work closely with application, analytics, and security teams to deliver integrated, end-to-end solutions.

• Mentorship:

  Lead and mentor cross-functional teams, fostering a culture of innovation, continuous improvement, and professional growth.

Technical Skills Required

• 12+ years

  in platform engineering, cloud, data, and AI roles

  , with deep expertise in AWS,

  Azure

  , GCP,

  Snowflake, Databricks,

  and leading

  AI/ML platforms.

• Proven experience architecting and migrating enterprise platforms (applications, data, AI/ML) to cloud.

• Mastery of cloud-native engineering (Kubernetes, Docker, Terraform), data engineering (Spark, Kafka, Airflow, DBT), and AI/ML platforms (Azure ML, SageMaker, Vertex AI, Databricks ML).

• Strong programming skills (Python, SQL, Spark, Java) and experience with cloud-native services and automation.

• Demonstrated experience with

  DataOps

  and

  MLOps

  : CI/CD, data quality, lineage, and observability tools.
• Hands-on experience embedding AI/ML into platforms and enabling advanced analytics.
• Data security, IAM, encryption, and regulatory compliance expertise.
• Leadership and consulting experience, with strong communication and executive presentation skills.

Nice-to-Have

• Certifications in AWS, Azure, GCP, Snowflake, Databricks, or leading AI/ML platforms.
• Experience with open-source platform, data, or AI engineering tools.
• Background in app modernization, MLOps, or cloud security.