Adactin - Senior Artificial Intelligence Engineer - AWS

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 solutions.

Responsibilities :

- 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 Specialty

- AWS Certified Machine Learning Specialty

- AWS Certified Solutions Architect Associate/Professional

- Experience 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.