Job description
Job Description
Key Responsibilities:
Data Engineering:
· Data LakeHouse:
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 Formation
· Knowledge of SQL and experience with the built-in SQL editor in Unified Studio for data querying
· AWS 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 & frameworks Able 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 positivity Agile self-determination, trust, transparency, and open collaboration All Support needed for the realization of business goals, Stable employment with a great atmosphere and ethical corporate culture
Required Skill Profession
Computer Occupations