ROLE -  SA AIML 

 Location - Bangalore 

 Years of experience needed – 8-10 years of relevant experience

 

 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 & Tech Stack : 

1. Data Architecture and Engineering:     a. Hadoop Ecosystem:             Preferred: Cloudera Data Platform (CDP) or Data Fabric.             Tools: HDFS, Hive, Spark, HBase, Oozie.      b. Data Warehousing:             Cloud-based: Azure Synapse, Amazon Redshift, Google Big Query, Snowflake, Azure Synapsis and Azure DataBricks             On-premises: , Teradata, Vertica      c. Data Integration and ETL Tools:             Apache NiFi, Talend, Informatica, Azure Data Factory, Glue.

  2. 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. 

3. HPE Platforms:       Data Fabric, AI Essentials or Unified Analytics, HPE MLDM and HPE MLDE 

4. 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) 

5. 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