DevSecOps Engineer (MLOps / AIOps)

Job Information:

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

Job Overview:

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.

Key Responsibilities:

• 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.

Qualifications and Experience:

• 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.

Skills and Abilities Required:

• 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.

Interview Process

• 2 Technical Rounds