Job Description
Key Responsibilities :
Data Engineering :
o Simplify analytics and AI with unified data management across Amazon S3 data lakes, Amazon Redshift data warehouses, operational databases, and enterprise applications.
Data Processing :o Use Visual ETL jobs and unified notebooks to perform data analysis, preparation, and integration with data sources like Amazon S3, Redshift, and AWS RDS.
Orchestration and Automation :o Create and manage SageMaker Pipelines and Workflows to automate the ETL Pipelines, ML model continuous integration and continuous delivery (CI / CD) lifecycle.
Data Governance and Collaboration :o Work within collaborative projects in SageMaker Unified Studio, leveraging the SageMaker Catalog (built on Amazon DataZone) to securely share data and AI artifacts.
o Implement various features in the project like Lineage, Data Quality, Metadata forms, Business Glossaries etc.
AI / ML :
End-to-End MLOps :o Build, deploy, execute, and monitor complete data and AI workflows using SageMaker Unified Studio's integrated environment.
Model Development :o Develop, train, and evaluate ML and generative AI models using SageMaker's purpose-built tools and algorithms.
o Utilize advanced features such as HyperPod for distributed training, JumpStart for pre-trained models, and SageMaker Experiments for tracking model versions.
Generative AI Development :o Rapidly build and customize generative AI applications within the integrated Amazon Bedrock environment in Unified Studio.
o Implement advanced customization capabilities like Knowledge Bases, Guardrails, Agents, and Flows.
Security and Compliance :o Implement and maintain security best practices, including fine-grained access controls and data protection, within the SageMaker environment.
Performance Optimization :o Optimize the performance and cost of ML workloads by right-sizing compute resources and monitoring job metrics.
Troubleshooting :o Utilize Amazon Q Developer's generative AI-powered assistance for code generation and resolving issues within the notebooks and jobs.
Required Skills
Experience with data processing and analytics using services integrated with Sagemaker Unified Studio (e.g., AWS Glue, Amazon EMR, Amazon Athena, AWS Redshift).Demonstrable expertise with Amazon SageMaker Unified Studio and its core components (Domain Units, Projects, Data Assets, Jobs, Data Products).Proficiency in PySpark, Python and relevant ML / AI libraries such as TensorFlow, PyTorch, and Scikit-learn.Experience with generative AI application development using Amazon Bedrock services within Unified Studio.Proven experience developing machine learning models and data pipelines on AWS.Solid understanding of MLOps principles and experience building CI / CD pipelines for ML models.Familiarity with AWS security best practices, including IAM roles, policies, and data governance, AWS Lake FormationKnowledge of SQL and experience with the built-in SQL editor in Unified Studio for data queryingAWS Certified Data Engineer Associate / AWS Certified Machine Learning – Specialty or other relevant AWS certifications.Required Technical / Functional Competencies
Domain / Industry Knowledge :
Basic knowledge of customer's business processes- relevant technology platform or product.Able to prepare process maps, workflows, business cases and simple business models in line with customer requirements with assistance from SME and apply industry standards / practices in implementation with guidance from experienced team members.Requirement Gathering and Analysis :
Working knowledge of requirement management processes and requirement analysis processes, tools and methodologies.Able to analyse the impact of change requested / enhancement / defect fix and identify dependencies or interrelationships among requirements and transition requirements for the engagement.Product / Technology Knowledge :
Working knowledge of technology product / platform standards and specifications.Able to implement code or configure / customize products and provide inputs in design and architecture adhering to industry standards / practices in implementation.Analyse various frameworks / tools, review the code and provide feedback on improvement opportunities.Architecture tools and frameworks :
Basic knowledge of architecture Industry tools & frameworksAble to analyse available tools and frameworks for review by the SME and plan for tool configurations and development.Architecture concepts and principles :
Basic knowledge of architectural elements, SDLC, methodologies.Able to apply various architectural constructs in the projects and identify various architectural patterns and implement.Analytics Solution Design :
High-level awareness of a wide range of core data science / analytics techniques, their advantages, disadvantages, and areas of application.Tools & Platform Knowledge :
Familiar with wide range of mainstream commercial and open-source data science / analytics software tools, their constraints, advantages, disadvantages, and areas of application.Required Behavioral Competencies
Accountability :
Takes responsibility for and ensures accuracy of own work, as well as the work and deadlines of the team.Collaboration :
Participates in team activities and reaches out to others in team to achieve common goals.Agility :
Demonstrates a willingness to accept and embrace differing ideas or perceptions which are beneficial to the organization.Customer Focus :
Displays awareness of customers stated needs and gives priority to meeting and exceeding customer expectations at or above expected quality within stipulated time.Communication :
Targets communications for the appropriate audience, clearly articulating and presenting his / her position or decision.Drives Results :
Sets realistic stretch goals for self & others to achieve and exceed defined goals / targets.Certifications
Good To Have At YASH, you are empowered to create a career that will take you to where you want to go while working in an inclusive team environment. We leverage career-oriented skilling models and optimize our collective intelligence aided with technology for continuous learning, unlearning, and relearning at a rapid pace and scale. Our Hyperlearning workplace is grounded upon four principles
Flexible work arrangements, Free spirit, and emotional positivityAgile self-determination, trust, transparency, and open collaborationAll Support needed for the realization of business goals,Stable employment with a great atmosphere and ethical corporate culture