Devsecops Engineer

Position DevSecOps Engineer MLOps AIOps 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 Location IN-UP-Noida India-World Trade Tower eInfochips Time Type Full time Job Category Engineering Services