DevSecOps Engineer (MLOps / AIOps)

Work Experience: 5+ Years
Industry: IT Services Job Type: FULL TIME Location: Noida, India

SourceFuse is seeking an innovative and forward-thinking AI-Driven DevSecOps Engineer (MLOps / AIOps) to integrate Agentic AI within the Software Development Lifecycle (SDLC). This role emphasizes embedding security, automation, and intelligence into every stage of development, leveraging AI-native tools, GitHub Copilot, Amazon Q, Bedrock, and other AWS ML services to accelerate delivery, enhance decision-making, and maintain compliance.

• 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.