Siemens Asset Management Software - Senior/Staff MLOps Engineer

Note : If screened-in, you will be invited for initial rounds on 1st August 2026 (Saturday) in Gurugram.About Brightly Software :Brightly Software is a leader in intelligent asset management and operational optimization, empowering organizations with data-driven insights. As we expand our AI and ML capabilities, we are seeking a Senior MLOps Engineer to build and scale the infrastructure that powers our next generation of predictive and autonomous solutions.Role Overview :As a Senior MLOps Engineer, you will architect, develop, and operate end-to-end machine learning infrastructure on AWS. You will work at the intersection of ML engineering, cloud infrastructure, and developer productivity-enabling Brightly's data science teams to move seamlessly from experimentation to reliable, secure, and cost-efficient production systems.Your work will ensure that ML models and data pipelines are scalable, observable, and compliant with best-in-class MLOps practices.Key Responsibilities :ML Platform & Infrastructure (AWS-focused) :- Design, build, and operate ML/AI development platforms on AWS, leveraging services such as Amazon SageMaker (Studio, Training, Real-Time & Async Inference, Pipelines, Feature Store), S3, Glue, Lambda, ECS/EKS, and related cloud infrastructure.- Implement infrastructure-as-code using Terraform or equivalent, and manage workflow orchestration using AWS Step Functions or Airflow.Data & Model Pipelines :- Build automated data ingestion and transformation pipelines using S3, Glue, EMR/Spark, and Redshift, incorporating data quality and lineage tooling (e.g., Great Expectations, Deequ).CI/CD for Machine Learning :- Develop CI/CD pipelines for ML with CodeBuild, CodePipeline, or GitHub Actions, integrating unit tests, data contract checks, model validation, canary/shadow deployments, and automated rollback strategies.Model Deployment & Operations :- Deploy real-time inference endpoints (SageMaker endpoints or FastAPI-based services on Lambda/ECS/EKS) and scalable batch processing jobs.- Define SLOs, implement autoscaling, and drive cost/performance optimizations across ML workloads.Monitoring, Observability & Governance :- Implement production monitoring for drift, bias, and performance using SageMaker Model Monitor and service telemetry tools like CloudWatch, Prometheus, and Grafana.- Enforce security and governance best practices, including least-privilege IAM, VPC-isolated architectures, encryption, and secret management.Cross-Functional Collaboration :- Partner closely with data scientists, ML engineers, and backend engineers to productionize ML models and streamline development workflows.- Contribute to the integration of emerging GenAI workloads, including Amazon Bedrock, vector databases (e.g., OpenSearch), and RAG pipelines.Required Qualifications :- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.- 10+ years of professional experience in ML engineering, DevOps, cloud engineering, or MLOps roles, with at least 3 years in a senior or lead capacity.- 3+ years of proven track record in designing and architecting robust, scalable ML systems and infrastructure in cloud environments, particularly on AWS.- 5+ years of deep experience building on the AWS ML ecosystem, including SageMaker, S3, Lambda, ECR, EKS/ECS, Step Functions, IAM, VPC networking, and CI/CD tooling.- 3+ years of hands-on experience deploying, maintaining, and scaling ML models in production environments.- 3+ years of strong Python development skills and familiarity with Docker-based workflows.- 5+ years of solid understanding of ML lifecycles, model evaluation, and monitoring patterns.- 5+ years of extensive experience with infrastructure-as-code (Terraform, CloudFormation).- 5+ years of expertise in designing system architecture for ML platforms, including microservices, container orchestration, and cloud networking.- 3+ years of familiarity with MLOps best practices as defined by AWS and industry standards.- 2+ years of experience with data quality frameworks (Great Expectations, Deequ).- 2+ years of experience optimizing distributed training workflows on AWS.- 3+ years of knowledge of security and compliance requirements for ML in enterprise settings, such as IAM, encryption, and secret management.- 2+ years of experience with monitoring tools (CloudWatch, Prometheus, Grafana) and implementing model observability solutions.- 5+ years of effective cross-functional collaboration skills, working closely with data scientists, ML engineers, and software engineers to deliver production-grade ML solutions.- 7+ years of excellent problem-solving and communication abilities, with a focus on delivering scalable, reliable, and cost-effective ML platforms. (ref:hirist.tech)