Delivery Lead Data AI

SUMMARY

Key Responsibilities:

Work closely with clients to understand their business requirements and design data solutions that meet their needs.

Develop and implement end-to-end data solutions that include data ingestion, data storage, data processing, and data visualization components.

Design and implement data architectures that are scalable, secure, and compliant with industry standards.

Work with data engineers, data analysts, and other stakeholders to ensure the successful delivery of data solutions.

Participate in presales activities, including solution design, proposal creation, and client presentations.

Act as a technical liaison between the client and our internal teams, providing technical guidance and expertise throughout the project lifecycle.

Stay up-to-date with industry trends and emerging technologies related to data architecture and engineering.

Develop and maintain relationships with clients to ensure their ongoing satisfaction and identify opportunities for additional business.

Understands Entire End to End AI Life Cycle starting from Ingestion to Inferencing along with Operations.

Exposure to Gen AI Emerging technologies.

Exposure to Kubernetes Platform and hands on deploying and containorizing Applications.

Good Knowledge on Data Governance, data warehousing and data modelling.

Requirements:

Bachelor's or Master's degree in Computer Science, Data Science, or related field.

10+ years of experience as a Data Solution Architect, with a proven track record of designing and implementing end-to-end data solutions.

Strong technical background in data architecture, data engineering, and data management.

Extensive experience on working with any of the hadoop flavours preferably Data Fabric.

Experience with presales activities such as solution design, proposal creation, and client presentations.

Familiarity with cloud-based data platforms (e.g., AWS, Azure, Google Cloud) and related technologies such as data warehousing, data lakes, and data streaming.

Experience with Kubernetes and Gen AI tools and tech stack.

Excellent communication and interpersonal skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences.

Strong problem-solving skills, with the ability to analyze complex data systems and identify areas for improvement.

Strong project management skills, with the ability to manage multiple projects simultaneously and prioritize tasks effectively.

Tools and Tech Stack

Data Architecture and Engineering:

Hadoop Ecosystem:

Preferred: Cloudera Data Platform (CDP) or Data Fabric.

Tools: HDFS, Hive, Spark, HBase, Oozie.

Data Warehousing:

Cloud - based: Azure Synapse, Amazon Redshift, Google Big Query, Snowflake, Azure Synapsis and Azure Data Bricks

On - premises: , Teradata, Vertica

Data Integration and ETL Tools:

Apache NiFi, Talend, Informatica, Azure Data Factory, Glue.

Cloud Platforms:

Azure (preferred for its Data Services and Synapse integration), AWS, or GCP.

Cloud - native Components:

Data Lakes: Azure Data Lake Storage, AWS S3, or Google Cloud Storage.

Data Streaming: Apache Kafka, Azure Event Hubs, AWS Kinesis.

HPE Platforms:

Data Fabric, AI Essentials or Unified Analytics, HPE MLDM and HPE MLDE

AI and Gen AI Technologies:

AI Lifecycle Management:

MLOps: MLflow, KubeFlow, Azure ML, or SageMaker, Ray

Inference tools: TensorFlow Serving, K Serve, Seldon

Generative AI:

Frameworks: Hugging Face Transformers, LangChain.

Tools: OpenAI API (e.g., GPT-4)

Orchestration and Deployment:

Kubernetes:

Platforms: Azure Kubernetes Service (AKS)or Amazon EKS or Google Kubernetes Engine (GKE) or Open Source K8

Tools: Helm

CI/CD for Data Pipelines and Applications:

Jenkins, GitHub Actions, GitLab CI, or Azure DevOps