Job title : Senior AI Engineer with AWS
Location : WFH, India
Experience : 10-15 years
About the role :
We are looking for an experienced AWS Data & AI Engineer to design and implement scalable data and AI solutions on AWS. The role involves building end-to-end data pipelines, developing Lakehouse architectures, and operationalizing ML models using services like S3, Glue, Redshift, and SageMaker. Youll work closely with cross-functional teams to ensure data reliability, performance, and security while driving innovation through cloud-based analytics and AI :
- Design and implement end-to-end data architectures on AWS including ingestion, storage, transformation, and analytics pipelines.
- Build data lake and data warehouse solutions using services such as AWS S3, Glue, Redshift, Lake Formation, and Athena.
- Develop and maintain ETL / ELT pipelines using AWS Glue, Step Functions, Lambda, or EMR.
- Collaborate with data scientists to operationalize AI / ML models using Amazon SageMaker, integrating them with enterprise data sources.
- Implement real-time and batch data processing frameworks leveraging Kinesis, Kafka on AWS MSK, or Spark on EMR.
- Ensure data security and compliance through IAM, KMS, CloudTrail, and governance policies.
- Design CI / CD pipelines for data and AI solutions using CodePipeline, CodeBuild, or GitHub Actions.
- Monitor and optimize data workloads for cost efficiency, scalability, and performance.
- Work closely with business stakeholders to translate requirements into technical specifications and delivery roadmaps.
Required Skills & Experience :
Proven experience with AWS cloud services for data and AI including S3, Glue, Redshift, Athena, SageMaker, Lambda, and EMR.Strong proficiency in Python, SQL, and PySpark for data transformation and ML workflows.Experience designing data Lakehouse architectures and integrating with BI tools (e.g., QuickSight, Power BI, Tableau).Familiarity with MLOps and DataOps practices (CI / CD, model deployment, monitoring, retraining).Solid understanding of data modelling, metadata management, and governance.Hands-on experience with infrastructure-as-code (IAC) tools like Terraform or AWS CloudFormation.Excellent problem-solving, communication, and stakeholder management skills.Preferred Qualifications :
AWS Certifications such as :
AWS Certified Data Analytics SpecialtyAWS Certified Machine Learning SpecialtyAWS Certified Solutions Architect Associate / ProfessionalExperience with Databricks on AWS, Snowflake, or AI frameworks (TensorFlow, PyTorch, Hugging Face).Background in analytics enablement, data governance, or cloud cost optimization.Knowledge of GenAI, LLM fine-tuning, and AI agent integration using AWS Bedrock or SageMaker JumpStart.(ref : hirist.tech)