DevSecOps Engineer
2 days ago
- Lead the foundational setup of the organization's MLOps and AIOps functions — including architecture, tooling, governance, and workflows — aligned with business and data strategy.
- Define and implement the MLOps roadmap: identify key use cases, standardize model development lifecycle, and architect scalable pipelines using AWS-native services.
- Establish the AIOps practice by implementing observability, automated incident response, and intelligent monitoring using Amazon CloudWatch, DevOps Guru, AWS X-Ray, and other tools.
- Select and integrate appropriate AWS services (e.g., SageMaker, Bedrock, Lambda, Glue, CodePipeline) to build a flexible and secure MLOps and AIOps infrastructure.
- Collaborate with stakeholders across data science, DevOps, engineering, and security to ensure the smooth operationalization of AI/ML models from development to production.
- Develop governance, monitoring, and compliance frameworks for AI/ML lifecycle, ensuring data security, lineage, traceability, and auditability.
- Design and implement model CI/CD workflows from scratch using tools like SageMaker Pipelines, CodeBuild, Terraform, GitHub Actions, and CloudFormation.
- Build out data pipelines and feature engineering workflows on AWS using services like AWS Glue, S3, Step Functions, Athena, and Redshift.
- Establish and evangelize best practices for model monitoring, drift detection, and continuous retraining using SageMaker Model Monitor, Clarify, and CloudWatch.
- Lead the adoption of AI copilots and Agentic AI (e.g., GitHub Copilot, Amazon Q, Bedrock agents) to improve developer productivity, code quality, and automation.
- Develop a strategy for real-time data observability and health monitoring using open-source and AWS-native tools, integrated into the broader data ecosystem.
- Provide thought leadership and mentoring to build internal capability in MLOps and AIOps, including training team members and defining reusable standards.
- Define and implement security-first principles for ML pipelines, ensuring IAM policies, encryption, and secrets management follow AWS best practices.
- Drive adoption of infrastructure-as-code (IaC) for ML platform provisioning and reproducibility using Terraform or CloudFormation.
- Bachelor's degree in Software Engineering, Computer Science, Computer Engineering, or a related engineering discipline; Master's degree (preferably from IIT/IISc or other premier institutes) is a strong advantage.
- 5+ years of hands-on experience in designing, implementing, or supporting AI/ML workloads on AWS, with a strong focus on cloud-native architecture, automation, and operationalization.
- Expertise in scripting/development using Python, AWS SDKs (boto3), Bash, and infrastructure scripting languages. While following development best practices.
- Expertise in Terraform & CloudFormation.
- Experience with AWS Services like STS, IAM, Lambda, S3, CloudWatch, Glue, SageMaker, QuickSight, Athena, Bedrock, EventBridge, etc.
- Experience integrating Agentic AI systems into software development workflows
- CI/CD security scanning, compliance enforcement, observability
- Good Communication and documentation skills.
- Can-do positive attitude, always looking to accelerate development.
- Driven commit to high standards of performance and demonstrate personal ownership for getting the job done.
- Innovative and entrepreneurial attitude, stays up to speed on all the latest technologies and industry trends; healthy curiosity to evaluate, understand and utilize new technologies.
Good to have:
- Experience with Amazon Q, Bedrock Agents, or similar generative AI agents.
- Development experience in python.
- Exposure to open-source observability stacks like Prometheus, Grafana, and ELK.
- Knowledge of enterprise data governance frameworks and tools (e.g., AWS Lake Formation, Apache Atlas).
- AWS Certifications such as: AWS Certified Machine Learning – Specialty, AWS Certified Data Analytics – Specialty, AWS Certified Solutions Architect – Associate or Professional
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