Platform (Cloud, Data & AI) Practice Lead

Platform (Cloud, Data & AI) Practice LeadOrganization:Amdocs (Cloud Studios) Location:India, Pune Reports to:Head of CoE and Global DeliveryRole 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+ yearsin platform engineering, cloud, data, and AI roles , with deep expertise in AWS,Azure , GCP,Snowflake, Databricks,and leadingAI/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 withDataOpsandMLOps : 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.