Platform (Cloud, Data & AI) Practice Lead
Organization: Amdocs (Cloud Studios)
Location: India, Pune
Reports to: Head of CoE and Global Delivery
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.
